Updated on 2024/07/15

写真a

 
GOTO, Masayuki
 
Affiliation
Faculty of Science and Engineering, School of Creative Science and Engineering
Job title
Professor
Degree
博士(工学) ( 早稲田大学 )

Research Experience

  • 2011.04
    -
    Now

    Waseda University   理工学術院 創造理工学部 経営システム工学科   Professor

  • 2008.09
    -
    2011.03

    Waseda University   理工学術院 創造理工学部 経営システム工学科   Associate Professor

  • 2002.04
    -
    2008.09

    Musashi Institute of Technology   環境情報学部   Associate Professor

  • 2000.03
    -
    2002.03

    March, Research Associate, The University of Tokyo   大学院 工学系研究科 環境海洋工学専攻   Research Associate

  • 1996.04
    -
    1999.03

    April, Research Associate, Waseda University   理工学部   Research Associate

Committee Memberships

  • 2019.04
    -
    Now

    私立大学情報教育協会  データサイエンス教育分科会 委員

  • 2005.04
    -
    Now

    私立大学情報教育協会  経営工学教育IT活用研究委員会 委員

  • 2019.05
    -
    2021.05

    日本経営工学会  「経営システム」誌編集委員会 委員長

  • 2017.05
    -
    2021.05

    日本経営工学会  表彰委員会 委員

  • 2020.06
    -
    2020.12

    日本学術振興会  令和2年度 知識集約型社会を支える人材育成事業委員会 専門委員

  • 2017.05
    -
    2019.05

    日本経営工学会  経営システム誌編集委員

  • 2016.04
    -
    2018.04

    電子情報通信学会  基礎境界ソサイエティ出版委員

  • 2015.05
    -
    2017.05

    日本経営工学会  第33期 監事

  • 2013.05
    -
    2015.05

    日本経営工学会  第32期 理事(論文誌・「経営システム」誌編集担当)

  • 2014.07
    -
    2014.12

    電子情報通信学会 情報理論とその応用サブソサイエティ  第37回情報理論とその応用シンポジウム 実行委員

  • 2011.05
    -
    2013.05

    日本経営工学会  第31期 理事(論文誌編集担当)

  • 2009.05
    -
    2011.05

    日本経営工学会  論文誌編集 副委員長

  • 2004.05
    -
    2011.05

    日本経営工学会  大会委員

  • 2005.05
    -
    2009.05

    日本経営工学会  論文誌編集委員

  • 2008.05
    -
    2008.10

    電子情報通信学会 情報理論とその応用サブソサイエティ  第31回情報理論とその応用シンポジウム 実行委員

  • 2005.05
    -
    2007.05

    日本経営工学会  第28期評議員

  • 2004.12
    -
    2005.10

    電子情報通信学会  英文論文誌 2005年SITA特集号 編集委員

  • 2004.09
    -
    2005.06

    経営情報学会  2005年春季全国研究発表大会 実行委員

  • 2004.04
    -
    2005.05

    経済産業省  平成16年度 住宅産業関連ニュービジネス支援事業 検討調査委員

  • 2004.07
    -
    2004.12

    情報理論とその応用シンポジウム  第27回情報理論とその応用シンポジウム プログラム委員

  • 2000.05
    -
    2003.05

    日本経営工学会  「経営システム」誌 編集委員

  • 2001.04
    -
    2003.03

    ビジネスモデル学会  運営委員会 副幹事

  • 2000.04
    -
    2002.04

    ビジネスモデル学会  設立準備委員 事務局

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Professional Memberships

  •  
     
     

    IEEE

  •  
     
     

    INFORMATION PROCESSING SOCIETY OF JAPAN

  •  
     
     

    THE INSTITUTE OF ELECTRONICS, INFORMATION AND COMMUNICATION ENGINEERS

  •  
     
     

    JAPAN INDUSTRIAL MANAGEMENT ASSOCIATION

  •  
     
     

    THE JAPANESE SOCIETY FOR ARTIFICIAL INTELLIGENCE

  •  
     
     

    THE JAPAN SOCIETY FOR MANAGEMENT INFORMATION

  •  
     
     

    THE OPERATIONS RESEARCH SOCIETY OF JAPAN

  •  
     
     

    THE JAPANESE SOCIETY FOR QUALITY CONTROL

  •  
     
     

    THE JAPANESE SOCIETY FOR ENVIRONMENTAL EDUCATION

  •  
     
     

    INFORMS

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Research Areas

  • Social systems engineering / Statistical science / Intelligent informatics / Theory of informatics

Research Interests

  • Data science

  • Business analytics

  • Machine learning

  • Pattern recognition

  • Statistical learning theory

  • Marketing analysis

  • Information statistics

  • Industrial engineering

  • Information theory

  • Management information

  • Informatics

  • Information system

  • Text Mining

  • Data Mining

  • Data analysis

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Awards

  • Outstanding Paper Award, APIEMS2023

    2023.10   23rd Asia Pacific Industrial Engineering & Management System Conference (APIEMS 2023)   "An improved algorithm based on self-supervised learning for multi-tag prediction of extreme weather events"

    Winner: Tomoki Amano, Ryotaro Shimizu, Masayuki Goto, Tomohiro Yoshikai

  • Best Paper Award, APIEMS 2022

    2022.11   The 22nd Asia Pacific Industrial Engineering and Management Systems (APIEMS 2022)   "A New Analytical Model for Product Reviews Based on BERT Feature Extraction"

    Winner: Koutarou Yamashita, Ayako Yamagiwa, Kyosuke Hasumoto, Masayuki Goto

  • Best Paper Award, The 20th ANQ Congress 2022

    2022.10   Asian Network for Quality   ”A Method to Improve Serendipity of Recommendation Lists Based on Collaborative Metric Learning”

    Winner: Akiko Yoneda, Ryota Matsunae, Haruka Yamashita, Masayuki Goto

  • 2022 PCカンファレンス 優秀論文賞

    2022.08   コンピュータ利用教育学会(CIEC)   ”反転ゼミ形式による多大学で連携するオンライン研究交流の試み ― データサイエンス領域のオンラインゼミを事例として ―”

    Winner: 後藤正幸, 小林 学, 守口 剛, 関 庸 一, 鈴木秀男, 生田目 崇, 中田和秀, 石垣 綾, 上田雅夫, 佐藤公俊, 三川健太, 山下 遥, 田尻 裕

  • Best Paper Award, JASMIN 2021

    2021.11   The Japan Society for Management Information   Relationship Analysis of Query and Answer Documents Based on Latent Dirichlet Allocation and Its Application

    Winner: Jyunya Ohkawa, Gendo Kumoi, Masayuki Goto

  • Best Paper Award, The 19th ANQ Congress 2021

    2021.10   Asian Network for Quality   "An Improved Method for Estimating Conditional Average Treatment Effects Taking Account of Selection Bias Based on Causal Tree"

    Winner: Yuki Tsuboi, Yuta Sakai, Satoshi Suzuki, Masayuki Goto

  • Encouragement Paper Award, AAMSA

    2021.03   Asian Association of Management Science and Applications(AAMSA)   "Factorization Machines Considering the Latent Characteristics behind Target Data"

    Winner: Tomoya Sugisaki, Kenta Mikawa, Masayuki Goto

  • IDRユーザフォーラム2020 企業賞,DBSJ特別賞(ダブル受賞)

    2020.11   国立情報学研究所   ”Knowledge Graph Attention Networkに基づく購買行動分析モデルに関する一考察”

    Winner: 伊藤史世, 張 志穎, 雲居玄道, 後藤正幸

  • Best Paper Award, APIEMS 2019

    2019.12   The 20th Asia Pacific Industrial Engineering and Management Systems (APIEMS 2019)   "Predicting Customer Churn of a Platform Business Using Latent Variables of Variational Autoencoder and Analysis of Customers’ Purchasing Behaviors"

    Winner: Kyosuke Hasumoto, Masayuki Goto

  • Best Paper Award, ANQ Congress 2018

    2018.09   16th Asian Network for Quality Congress (ANQ2018)   "A Study on Feature Clustering Analysis By the Hidden Layer of Autoencoder"

    Winner: Shimpei Kanazawa, Yuuki Sugiyama, Tianxiang Yang, Masayuki Goto

  • The World CIST'18 Best Paper Award

    2018.03   6th World Conference on Information Systems and Technologies (WorldCist'18)   "System Evaluation of Construction Methods For Multi-class Problems Using Binary Classifiers"

    Winner: Shigeichi Hirasawa, Gendo Kumoi, Manabu Kobayashi, Masayuki Goto, Hiroshige Inazumi

  • Best Paper Award, ANQ Congress 2017

    2017.09   15th Asian Network for Quality Congress (ANQ2017)   "A New Analytical Model for Customer Growth Considering Potential Purchasing Preferences"

    Winner: Yuri Nishio, Hiroaki Ito, Haruka Yamashita, Masayuki Goto

  • 平成28年度 科研費審査員表彰

    2016.11   Japan Society for the Promotion of Science  

    Winner: Masayuki Goto

  • JIMA Distinguished Research Award

    2015.05   Japan Industrial Management Association  

    Winner: Masayuki Goto

  • Best Paper Award, Journal of JIMA

    2015.05   Japan Industrial Management Association  

    Winner: Takahiro Ooi, Kenta Mikawa, Masayuki Goto

  • IPSJ第77回全国大会 優秀賞

    2015.03   Information Processing Society of Japan  

    Winner: 中澤 真, 梅澤克之, 小林 学, 小泉大城, 後藤正幸, 平澤茂一

  • IPSJ第74回全国大会 優秀賞

    2012.03   Information Processing Society of Japan   ”マルコフモデルによる自動分類に対する分類誤り確率の推定"

    Winner: 小林 学, 後藤正幸, 松嶋敏泰, 平澤茂一

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Papers

  • An Improved Search Algorithm Based on Safe Exploration for Optimization to Control Worsening of Objective Function Value

    Yuka Nakamura, Taiga Yoshikawa, Ayako Yamagiwa, Masayuki Goto

    Industrial Engineering and Management Systems   23 ( 2 ) 125 - 135  2024.07  [Refereed]

    Authorship:Last author

    DOI

  • An Estimation Model of Intrinsic Evaluation Ratings by Customer Reviews Based on BERT Feature Extraction

    Kotaro Yamashita, Ayako Yamagiwa, Kyosuke Hasumoto, Masayuki Goto

    Industrial Engineering and Management Systems   23 ( 2 ) 182 - 194  2024.07  [Refereed]

    Authorship:Last author

    DOI

  • Optimizing FT-Transformer: Sparse Attention for Improved Performance and Interpretability

    Tokimasa Isomura, Ryotaro Shimizu, Masayuki Goto

    Industrial Engineering and Management Systems   23 ( 2 ) 253 - 266  2024.07  [Refereed]

    Authorship:Last author

    DOI

  • SCP: Spherical-Coordinate-Based Learned Point Cloud Compression

    Ao Luo, Linxin Song, Keisuke Nonaka, Kyohei Unno, Heming Sun, Masayuki Goto, Jiro Katto

    Proceedings of the AAAI Conference on Artificial Intelligence   38 ( 4 ) 3954 - 3962  2024.03  [Refereed]

     View Summary

    In recent years, the task of learned point cloud compression has gained prominence. An important type of point cloud, the spinning LiDAR point cloud, is generated by spinning LiDAR on vehicles. This process results in numerous circular shapes and azimuthal angle invariance features within the point clouds. However, these two features have been largely overlooked by previous methodologies. In this paper, we introduce a model-agnostic method called Spherical-Coordinate-based learned Point cloud compression (SCP), designed to leverage the aforementioned features fully. Additionally, we propose a multi-level Octree for SCP to mitigate the reconstruction error for distant areas within the Spherical-coordinate-based Octree. SCP exhibits excellent universality, making it applicable to various learned point cloud compression techniques. Experimental results demonstrate that SCP surpasses previous state-of-the-art methods by up to 29.14% in point-to-point PSNR BD-Rate.

    DOI

    Scopus

    1
    Citation
    (Scopus)
  • An Efficient Approach to Parameter Estimation in Multivariate Polynomial Regression Models via Regularized Least Squares

    Kazuma Inoue, Ryotaro Shimizu, Tota Suko, Masayuki Goto

    IPSJ Journal   17 ( 1 ) 36 - 46  2024.02  [Refereed]

    Authorship:Last author

  • Effectiveness verification framework for coupon distribution marketing measure considering users’ potential purchase intentions

    Akiko Yoneda, Ryotaro Shimizu, Shion Sakurai, Makoto Kawata, Haruka Yamashita, Masayuki Goto

    Cogent Engineering   11 ( 1 ) 2307718  2024.01  [Refereed]

    Authorship:Last author

     View Summary

    In recent years, web marketing has thrived, and online coupon distribution has become a significant marketing measure that leads to increased sales. However, randomly distributing coupons risks lowering the profit ratio of companies. Therefore, it is important to estimate the effect of coupons and analyze the causal relationship between coupons and results. The potential purchase intention (PPI) of users is believed to influence the effect of coupons. For example, distributing coupons to users with a low PPI is likely to increase the gross profit of companies, whereas distributing coupons to users with a high PPI is likely to decrease the gross profit. Therefore, by analyzing the relationship between PPI and the effect of coupons, highly effective targeting can be conducted based on the PPI. In this paper, we propose an experimental design based on machine learning to analyze the effect of coupons, which varies depending on the PPI. We propose a method to predict users’ PPI based on their purchase history data using machine learning and analyze the relationship between PPI and the effect of coupons. Finally, we demonstrate the effectiveness of the proposed framework by applying it to real-world data.

    DOI

    Scopus

  • Multiple treatment effect estimation for business analytics using observational data

    Yuki Tsuboi, Yuta Sakai, Ryotaro Shimizu, Masayuki Goto

    Cogent Engineering   11 ( 1 ) 2300557  2024.01  [Refereed]

    Authorship:Last author

     View Summary

    To correctly evaluate the effects of treatments, conducting randomized controlled trials (RCTs) is a reasonable approach. However, because it is generally difficult to implement RCTs for all treatments, methods to estimate the treatment effects using observational data have been actively studied and used in various decision-making processes. Observational data accumulated in business activities and elsewhere contains the results of various previously implemented treatments, and correctly estimating the effects of any given treatment without separating the impacts of other treatments is challenging. Against this background, this paper proposes a method to estimate the effects of multiple treatments of various types while considering various causal relationships. Specifically, the proposal is a variational inference method that estimates the effect of multiple treatments using four latent factors estimated from observations, making assumptions that are independent of the type and number of treatments. The proposed method makes it possible to appropriately estimate the effects of measures even in situations with complex causal relationships. In addition, in situations where measures with continuous parameters are being implemented, it is possible to estimate the effects of measures that have not been implemented in the past by treating the content of the measures as a continuous variable.

    DOI

    Scopus

    1
    Citation
    (Scopus)
  • A Model for Analyzing Changes in Child-rearing Life Stage Concerns based on Question Data on Dedicated App

    Koki Yamada, Yosuke Takao, Ayako Yamagiwa, Masayuki Goto

    Journal of Japan Industrial Management Association   74 ( 4 ) 153 - 166  2024.01  [Refereed]

    Authorship:Last author

     View Summary

    Dedicated applications (apps) for child-rearing question and answer (QA) system have become common tools widely used by many parents in Japan. A user can post his/her various questions about child-rearing on the systems and exchange information with other parents online. Since the value of such dedicated applications increases through being used by a large number of users, it is considered important to continuously provide attractive functions to increase user satisfaction. Here, concerns of parents should be changed depending on their children's life stages. In addition, it can be assumed that users always have concerns of child-rearing even when they do not post any questions on the application. Therefore, analysis and comprehension of the problems of parents rearing small children by taking into account these properties will lead to the improving the users' satisfaction of QA services. For instance, the app can provide users with proper information with appropriate timing based on the analysis result. Although topic models are widely known to analyze the content of text data, this is not a sufficient method for visualizing changes in topics over time from question text data, which are relatively short sentences posted on dedicated apps for child-rearing QA systems. In this study, the authors propose an analytical model that can extract the topics of users' concerns from question data and compile topic transitions throughout the various stages of child-rearing. Furthermore, by taking a probabilistic view of the topic transitions, a method for estimating the topic that is most likely to shift from a previous topic at a given time to the next non-question period is constructed. Finally, the authors show the results of applying the proposed method to an actual data set and clarify the usefulness of the estimation result using the proposed method. The proposed method makes it possible to analyze how the worries and concerns of parents raising children evolve over time, and to gain various new insights into the perspectives of support for these users through apps.

    DOI

    Scopus

  • A Method for Advanced Analysis of Item Browsing Data Considering User Interest Durations Using Parameters Estimated from Hidden Semi-Markov Model

    Kirin Tsuchiya, Yuki Tsuboi, Ryotaro Shimizu, Masayuki Goto

    Industrial Engineering & Management Systems   22 ( 4 ) 437 - 448  2023.12  [Refereed]

    Authorship:Last author

     View Summary

    Recently, competition among video streaming services for customers has intensified, so that it has become important to introduce appropriate marketing measure considering users’ preference and their purchasing behavior. In general, users’ purchasing actions for video content (items), unlike daily necessities, have a strong influence on their real-time transition of interests while viewing items (consumption). In other words, the user’s next purchasing intention after the consumption of an item is influenced by whether their interest is continued (probability of continued interest under the item). Therefore, it is important to select and evaluate items based on the probability of continued interest under items to allow users to use the service for a long time. For this purpose, the hidden semi-Markov model (HSMM), proposed as a model to predict the next item to be consumed by a user, can be a solution considering the user’s interest persistence. If the probabilities of continuing interest under each item can be calculated and analyzed by using the HSMM, new insights can be expected to lead to marketing strategies. In this study, we propose an analysis process based on item clustering using the probabilistic distribution of continued interests, using the characteristics of HSMM. We demonstrate the effectiveness of our proposed method by applying it to an actual dataset. The experimental results show that we can obtain the characteristics of the probability of continued interest under item for each class of items, and that we can evaluate items from a new perspective of the probability of continued interest under item.

    DOI

    Scopus

  • A Method to Improve Serendipity of Recommendation Lists Based on Collaborative Metric Learning

    Akiko Yoneda, Ryota Matsunae, Haruka Yamashita, Masayuki Goto

    Total Quality Science   9 ( 2 ) 62 - 73  2023.12  [Refereed]

    Authorship:Last author

    DOI

  • A Semi-Supervised Learning Model for Predicting User Attributes Based on Ladder Network

    Mizuki Takeuchi, Taichi Imafuku, Yuta Sakai, Masayuki Goto

    Total Quality Science   9 ( 2 ) 109 - 120  2023.12  [Refereed]

    Authorship:Last author

    DOI

  • Active Learning Method for Pairwise Comparison Data

    Ayako Yamagiwa, Masayuki Goto

    5th IEEE International Conference on Cybernetics, Cognition and Machine Learning Applications, ICCCMLA 2023     310 - 315  2023.10  [Refereed]

    Authorship:Last author

     View Summary

    Companies need to create a product position map and correctly grasp the current situation. In the recent increase in the number of e-commerce sites, the influence of product images on customers' willingness to purchase is huge. In other words, if we can construct a product map based on customers' impressions of product images, we can expect to make the product lineup on e-commerce sites more efficient and attractive. Semantic Differential (SD) method and Multi-dimensional scaling (MDS) have been conventionally used to create product maps. However, these methods are unsuitable for analyzing a target with a large number of products, such as EC sites, because the number of product evaluation data required for the analysis increases depending on the number of products. Therefore, the authors proposed a method to construct a product image map of product images with high accuracy from a relatively small number of pairwise comparison data. Specifically, a model that estimates the pairwise comparison data by subjects is machine-learned. The learned model is used to supplement the missing pairwise comparison data to estimate the evaluation values of product images for a specific axis. The values are used to construct a map. The method of selecting a small number of data used to train the model can quickly identify the overall trend. Therefore, this study proposes an adequate data selection criterion to improve the accuracy of the map based on the concept of active learning. Experiments show that, in addition to uncertainty considerations, it is adequate to have a uniform amount of pairwise comparison data for each subject in the problem of complementing pairwise comparison data between subjects, as in the case of this study.

    DOI

    Scopus

  • Recommendation Item Selection Algorithm Considering the Recommendation Region in Embedding Space and New Evaluation Metric

    Tomoki Amano, Ryotaro Shimizu, Masayuki Goto

    Industrial Engineering & Management Systems   22 ( 3 ) 340 - 348  2023.09  [Refereed]

    Authorship:Last author

     View Summary

    In recent years, recommender systems based on machine learning have become common tools on various web services. Among recommendation models, embedding methods such as Item2Vec that utilize embedded representations of items are widely used in actual applications due to their effectiveness and ease of use. By utilizing embedded representations acquired through learning the interaction between users and items, it is easy to discover similar items from the viewpoint of the user's purchasing tendencies. In contrast, with this method, only biased items are recommended, making it difficult to ensure a wide variety of recommended items. However, there is a trade-off between the diversity of recommended items and accuracy and providing diversity in recommended items while maintaining accuracy is a challenging problem. Therefore, in this study, we propose a method to expand the new evaluation metric "recommendation region"(sum of distances of recommended items from the user vector in the embedding space) without significantly reducing accuracy. Specifically, we recommend not only items that are close to the user vector in the embedding space but also items with a certain distance based on detailed observation of the positional relationships. With the proposed method, we aim to increase user satisfaction by expanding the diversity of items that the user comes into contact with in the service. Finally, we demonstrate the usefulness of our proposed method through evaluation experiments using open-source datasets.

    DOI

    Scopus

    2
    Citation
    (Scopus)
  • A Study of Diversity Analysis Model Based on Embeddings for Cooking Recipes

    Koutarou Yamashita, Fumiyo Ito, Kyosuke Hasumoto, Masayuki Goto

    Industrial Engineering & Management Systems   22 ( 3 ) 327 - 339  2023.09  [Refereed]

    Authorship:Last author

     View Summary

    Recently, a large number of cooking recipes have been posted and shared on the Internet. Various machine learning techniques have been proposed to analyze those recipes. Those include a method to discover alternative ingredients by obtaining distributed representations from cooking procedures and ingredient names, or a method to extract basic procedures from common features in cooking procedures. Such methods utilize the constructed semantic space to calculate the distances among cooking procedures and ingredients for recipes, and demonstrate effectiveness by evaluating similarity of recipes. Using a similar semantic space, we can analyze not only the similarities among recipes but also their diversity. Even for the same dish name, there could be a variety of recipes, depending on the contributor. The diversity of recipes varies from dish to dish. By taking this diversity into account, it is possible to perform various analyses such as extracting recipes that are suitable for each user. In this study, we propose a method to analyze the diversity of recipes using distributed representation. In addition, we apply the proposed method to the posted data on an actual recipe site and show its usefulness.

    DOI

    Scopus

  • 応用面から見る推薦システム

    後藤正幸

    人間生活工学   24 ( 2 ) 56 - 61  2023.09  [Invited]

    Authorship:Lead author, Corresponding author

  • A Robust Estimation Method for Conditional Average Treatment Effects Taking Account of Selection Bias Based on Causal Tree

    Yuki Tsuboi, Yuta Sakai, Satoshi Suzuki, Masayuki Goto

    IPSJ Journal   64 ( 9 ) 1399 - 1412  2023.09  [Refereed]

    Authorship:Last author

    DOI

  • A Model for Estimating the Operating Status of Individual Home Appliances Based on Bayesian Estimation from Main Power Consumption

    Satoshi Suzuki, Manabu Kobayashi, Masayuki Goto

    Journal of Japan Industrial Management Association   74 ( 2 ) 63 - 76  2023.07  [Refereed]

    Authorship:Last author

     View Summary

    The household electricity consumption data contains information on the lifestyles of each household unit, which is useful for marketing of consumer durables and services for households. In general, however, only the main power consumption can be metered using a single smart meter for each residence, and the operating status of home appliances is unknown from the companies’ viewpoint. Therefore, there have been several attempts to estimate the electricity consumption of each home appliance using dis-aggregation technology. However, there have been cost problems in practical use for exact estimation, such as the need for additional sensors. In this study, the authors formulate a problem specialized for estimating the operation and non-operation of home appliances using observed main energy data as household attribute information for marketing purposes. They then propose a state estimation model in a snapshot using the assumption of normal distributions for the electricity consumption of home appliances. Simulation experiments in a virtual housing environment show that the proposed model has an effective rate of correct answers in practical use. Additionally, a relationship between the proposed method and the prior probability distribution of device states that serve as input for the estimation is shown. The proposed model is a low-cost method for estimating household attributes that do not require additional sensors, and is expected to be used as basic information for marketing strategies.

    DOI

    Scopus

  • Partial Visual-semantic Embedding: Fine-grained Outfit Image Representation with Massive Volumes of Tags via Angular-based Contrastive Learning

    Ryotaro Shimizu, Takuma Nakamura, Masayuki Goto

    Knowledge-Based Systems   277   110791 - 110791  2023.07  [Refereed]

    Authorship:Last author

     View Summary

    A novel technology named fashion intelligence system, which quantifies ambiguous expressions unique to fashion, such as “casual,” “adult-casual,” and “office-casual,” was previously proposed to support users in their understanding of fashion. However, the existing visual-semantic embedding (VSE) model, which forms the basis of the system, does not support images that are composed of multiple parts, such as those containing hair, tops, trousers, skirts, and shoes. Therefore, we propose a partial VSE (PVSE) model, which enables fine-grained learning of each part of the fashion outfit. The proposed model learns embedded representations via angular-based contrastive learning. This helps in retaining three existing practical functionalities and further enables image-retrieval tasks where changes are only made to specified parts and image-reordering tasks focusing on the specified parts. In other words, the proposed model enables five types of practical functionalities, even with a simple structure. Through qualitative and quantitative experiments, we demonstrate that the proposed model is superior to conventional models, without increasing computational complexity.

    DOI

    Scopus

    2
    Citation
    (Scopus)
  • Store Analysis Using Latent Representation of Robust Variational Autoencoder Based on Sales History Data

    Ryogo Okubo, Ryosuke Uehara, Gendo Kumoi, Masayuki Goto, Tomohiro Yoshikai

    Total Quality Science   8 ( 2 ) 113 - 123  2023.06  [Refereed]  [Invited]

    Authorship:Last author

    DOI

  • A Study on Out-of-Distribution Detection based on Generative Models Trained for Each Discriminant Class

    Ryota Matsunae, Fuyu Saito, Haruka Yamashita, Masayuki Goto

    Total Quality Science   8 ( 2 ) 100 - 112  2023.06  [Refereed]  [Invited]

    Authorship:Last author

    DOI

  • Evaluation of the Recommendation Effect of Individual Interventions in a Recommender System

    Taichi Imafuku, Tatsuya Kawakami, Tianxiang Yang, Masayuki Goto

    Total Quality Science   8 ( 2 ) 77 - 88  2023.06  [Refereed]  [Invited]

    Authorship:Last author

    DOI

  • Fashion-Specific Ambiguous Expression Interpretation with Partial Visual-Semantic Embedding

    Ryotaro Shimizu, Takuma Nakamura, Masayuki Goto

    IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops   (CVPRW 2023)   3496 - 3501  2023.06  [Refereed]

    Authorship:Last author

     View Summary

    A novel technology named fashion intelligence system has been proposed to quantify ambiguous expressions unique to fashion, such as "casual,""adult-casual,"and "office-casual,"and to support users' understanding of fashion. However, the existing visual-semantic embedding (VSE) model, which is the basis of its system, does not support situations in which images are composed of multiple parts such as hair, tops, pants, skirts, and shoes. We propose partial VSE, which enables sensitive learning for each part of the fashion outfits. This enables five types of practical functionalities, particularly image-retrieval tasks in which changes are made only to the specified parts and image-reordering tasks that focus on the specified parts by the single model. Based on both the multiple unique qualitative and quantitative evaluation experiments, we show the effectiveness of the proposed model.

    DOI

    Scopus

  • Graph Embedding for Analyzing Business Communication between Employees

    Kenya Nonaka, Haruka Yamashita, Toyofumi Miura, Masayuki Goto

    IPSJ Journal   64 ( 3 ) 758 - 768  2023.03  [Refereed]

    Authorship:Last author

     View Summary

    On business chat applications that are widely used in many offices, communications between the employees are made in each group called channel. From the viewpoint of business analytics, the activation of human resource can be realized by grasping the real situation of the office community based on the analysis of the communication represented as the graph structure by utilizing the data stored on the business chat system. Especially, it is desirable to analyze the characteristics of business communication between employees for each channel, which is a group created for specific communication. For the purpose of analyzing employees’ communication, it is desirable to apply clustering or visualizing analyses of channels and it seems to possible if many channel graphs are converted to structured data. In this study, we improve the Deep Divergence Graph Kernel(DDGK) which converts the graph data to the structured data for chatting data analysis, and propose a method for obtaining embedding representation suitable to analyze the channel graphs. The proposed method enables to construct the embedding space which represent the important structure of channel graphs. Moreover, we show the effectiveness of the proposed model by applying to an actual communication data stored on Slack at a Japanese company. In this data analysis, feature vectors are activated by the model, and channel graphs can be classified by graph structures.

    J-GLOBAL

  • Performance Evaluation of Error-Correcting Output Coding Based on Noisy and Noiseless Binary Classifiers

    Gendo Kumoi, Hideki Yagi, Manabu Kobayashi, Masayuki Goto, Shigeichi Hirasawa

    International Journal of Neural Systems   33 ( 02 )  2023.02  [Refereed]  [International journal]

     View Summary

    Error-correcting output coding (ECOC) is a method for constructing a multi-valued classifier using a combination of given binary classifiers. ECOC can estimate the correct category by other binary classifiers even if the output of some binary classifiers is incorrect based on the framework of the coding theory. The code word table representing the combination of these binary classifiers is important in ECOC. ECOC is known to perform well experimentally on real data. However, the complexity of the classification problem makes it difficult to analyze the classification performance in detail. For this reason, theoretical analysis of ECOC has not been conducted. In this study, if a binary classifier outputs the estimated posterior probability with errors, then this binary classifier is said to be noisy. In contrast, if a binary classifier outputs the true posterior probability, then this binary classifier is said to be noiseless. For a theoretical analysis of ECOC, we discuss the optimality for the code word table with noiseless binary classifiers and the error rate for one with noisy binary classifiers. This evaluation result shows that the Hamming distance of the code word table is an important indicator.

    DOI PubMed

    Scopus

  • Time Window Topic Model for Analyzing Customer Browsing Behavior

    Fumiyo Ito, Gendo Kumoi, Masayuki Goto

    IPSJ Journal   64 ( 1 ) 214 - 228  2023.01  [Refereed]

    Authorship:Last author

     View Summary

    Nowadays, various services like EC sites have been expanding on the Internet, and huge amount of browsing history data are being accumulated. It is, therefore, desiblack to take effective marketing action by analyzing users' interest in detail by using accumulated browsing history data. Generally, users narrow down their interest while browsing pages and finally purchase an item on the EC site. Hence, to improve the effectiveness of marketing measures, it is important to analyze the change points of users' interest and to timely conduct it based on those change. Conventionally, Topic Tracking Model (TTM) has been proposed as a method which can model the changes of uses’ interest over time by purchase history data. However, TTM assumes the relatively long purchase history data and doesn’t consider the situation where users change their interest in short browsing site transition of about several dozen pages. Therefore, TTM cannot estimate the changes of users’ interest when applied to the browsing history data. In this research, we propose a new topic model which estimates parameters in step by step, named Time Window Topic Model. The proposed method enables us to estimate parameters and clarify the change of users' interest while browsing. It will be possible to conduct effective marketing measure based on the convergent of users' interest by applying the proposed model. Finally, we apply the proposed method to both of the artificial and real data set and show the usefulness of our proposed method.

    DOI

  • Adaptive Ranking-based Sample Selection for Weakly Supervised Class-imbalanced Text Classification

    Linxin Song, Jieyu Zhang, Tianxiang Yang, Masayuki Goto

    Findings of the 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP 2022)     1007  2022.12  [Refereed]  [International coauthorship]

    Authorship:Last author

    DOI

  • Correction to: Predicting customer churn for platform businesses: using latent variables of variational autoencoder as consumers’ purchasing behavior (Neural Computing and Applications, (2022), 34, 21, (18525-18541), 10.1007/s00521-022-07418-8)

    Kyosuke Hasumoto, Masayuki Goto

    Neural Computing and Applications   34 ( 21 ) 18543 - 18544  2022.11

     View Summary

    In this online published article, a format of few entries of Tables 4 to 7 were corrected. The Corrected tables were given below (Tables 4, 5, 6, 7): (Table presented.).

    DOI

    Scopus

  • Fashion intelligence system: An outfit interpretation utilizing images and rich abstract tags

    Ryotaro Shimizu, Yuki Saito, Megumi Matsutani, Masayuki Goto

    Expert Systems with Applications   213 ( Part C ) 119167 - 119167  2022.11  [Refereed]

    Authorship:Last author

     View Summary

    In recent years, it has become common for consumers to familiarize themselves with the latest fashion trends through the internet and engage in their own fashion-inspired shopping activities. Therefore, making fashion-inspired shopping and browsing activities (internet surfing in the fashion domain) comfortable is essential because it leads to interactions in the fashion industry. However, fashion is a fuzzy and complex domain that contains many abstract elements, and this ambiguity and complexity can hinder users’ deep interest in the fashion industry. Therefore, we define a novel technology and domain called “fashion intelligence” and propose a system based on a visual-semantic embedding method for automatically learning and interpreting fashion and obtaining answers to users’ questions. Our proposed method can embed the abundant abstract tag information in the same projective space as outfit images. Mapping of images and tags in a projective space helps search for outfit images using fashion-specific abstract words. In addition, visually estimating the degree of relevance between images and tags helps interpret abstract words. As a result, this research helps decrease fashion-specific ambiguity and complexity and supports the marketing activities and fashion choices of both experts and non-experts.

    DOI

    Scopus

    12
    Citation
    (Scopus)
  • Machine Learning for Demand Prediction of Seasonal Second-hand Fashion Items Based on Prior and Fine-tuning Prediction Models

    Fuyu Saito, Haruka Yamashita, Hokuto Sasaki, Masayuki Goto

    IPSJ Journal   63 ( 11 ) 1684 - 1696  2022.11  [Refereed]

    Authorship:Last author

     View Summary

    With the proliferation of E-commerce sites etc., distribution channels for second-hand fashion items are diversifying and its market is expanding. In the fashion industry, seasonal items that are in demand in particular seasons are very important. For them, excessive sales can lead to a decline in the attractiveness of the company and customer satisfaction due to subsequent shortages, but under-listing leads to the generation of bad inventory due to missed sales. To solve such trade-off problem, seasonal items need to be strategically listed by accurately predicting demand. Previously, planning and managing inventory, listing, and sales were often based on the experience and know-how of display managers. However, conventional judgments are impersonal and lack objectivity, and there are concerns that they do not solve the problem when the prediction differs from the actual. Therefore, this paper proposes a demand forecasting method that enables accurate planning of seasonal items based on more quantitative judgment. Furthermore, by applying the proposal to real data and analyzing it, we show the usefulness. In addition, we design and operate a demonstration experiment to use the method as an indicator for determining the number of new items to be listed and verify its effectiveness in real business.

    DOI

  • Construction Methods for Error Correcting Output Codes Using Constructive Coding and Their System Evaluations

    Shigeichi Hirasawa, Gendo Kumoi, Hideki Yagi, Manabu Kobayashi, Masayuki Goto, Hiroshige Inazumi

    2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2022)   2022-October   Paper We-PS2-T11.1 - 3059  2022.10  [Refereed]

     View Summary

    Consider M-valued (Mgeq3) classification systems realized by combination of N(Ngeqlceillog_{2}Mrceil) binary classifiers. Such a construction method is called an Error Correcting Output Code (ECOC). First, focusing on a Reed-Muller (RM) code, we derive a modified RM (mRM) code to make it suitable for the ECOC. Using the mRM code and the Hadamard matrix, we introduce a simplex code which is one of the powerful equidistant codes. Next, from the viewpoint of system evaluation model, we evaluate the ECOC by using constructive coding described above. We show that they have desirable properties such as Flexible, Elastic, and Effective Elastic as M becomes large, by employing analytical formulas and experiments.

    DOI

    Scopus

  • Learning and Estimation of Latent Structural Models Based on between-Data Metrics

    Kenta Mikawa, Manabu Kobayashi, Masayuki Goto, Shigeichi Hirasawa

    2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2022)   2022-October   Paper We-PS2-T12.5 - 3118  2022.10  [Refereed]

     View Summary

    With the development of information technology, a wide variety of data have been accumulated, and there are many methods for analyzing such data. In this study, we model the input data and the metrics between the data based on the assumption that each metric is generated from a continuous latent variable. Specifically, we assume that the input data are generated using low-dimensional latent variables and their projection matrices. We describe a method for estimating the latent variables. Because the generative model defined in this study cannot obtain the Q function analytically, we use the Monte Carlo EM algorithm to approximate the Q function and investigate an efficient parameter estimation method. Experiments using artificial data and the 20 newsgroups dataset demonstrate the effectiveness of the proposed method.

    DOI

    Scopus

  • A Study on Purchasing Behavior Analysis Method by Comparing Difference in Latent Class Distributions between Membership Stages

    Tianxiang Yang, Haruka Yamashita, Masayuki Goto

    Journal of Japan Industrial Management Association   73 ( 2 ) 54 - 69  2022.07  [Refereed]

    Authorship:Last author

     View Summary

    A marketing policy called the”Membership Stage System” is widely used in retail business. A membership stage provides benefits to customers such as shopping points when a customer's annual cumulative purchase amount exceeds a certain threshold and the customer's stage is raised a level. As a result, the company is not only able to promote the customer's willingness to purchase, but it can also obtain the purchasing history data, thereby enabling high-quality customer analysis. The most fundamental analysis is to infer the difference of purchasing characteristics between member stages and to construct different clustering models for each member stage. However, when the clustering models are learned independently for each membership stage, it is not possible to compare the obtained clusters between membership stages. In this study, we propose a new analytical method and its learning algorithm to analyze differences in cluster distribution between membership stages. Through demonstrating the proposed model applied to an actual data set of purchasing history data on a membership stage system, the effectiveness of our proposal is clarified.

    DOI J-GLOBAL

    Scopus

    1
    Citation
    (Scopus)
  • Transfer Learning Based on Probabilistic Latent Semantic Analysis for Analyzing Purchase Behavior Considering Customers' Membership Stages

    Tianxiang Yang, Gendo Kumoi, Haruka Yamashita, Masayuki Goto

    Journal of Japan Industrial Management Association   73 ( 2E ) 160 - 175  2022.07  [Refereed]

    Authorship:Last author

     View Summary

    In recent years, it has become common to analyze purchase history data and take advantage of the effect on business policies. In this study, the authors focus on the case of a company that is introducing a membership stage system. Probabilistic latent semantic analysis (PLSA) is well-known as an analytical model for analysis of co-occurrence of variables in data. However, the relationship between customers and items for the customer purchase behavior of each stage based on PLSA has not shown good performance. The purchase behaviors may be slightly different between customer stages, and accordingly, the purchase behavior of customers in different stages should be represented by a similar but different model. In addition, the higher the membership stage, the fewer customers there are; therefore, it becomes difficult to accurately understand the features of the customers' purchase behavior within high membership stages. In this study, the authors propose a learning algorithm that utilizes the estimated parameters of the behavior model based on the PLSA model at a lower-stage, to estimate the parameters of models at a higher-stage to which few people belong. Moreover, numerical simulation experiments are conducted and compared to actual purchase history data to confirm the performance of the proposed method.

    DOI

    Scopus

    4
    Citation
    (Scopus)
  • Evaluation of Analysis Model for Products with Coefficients of Binary Classifiers and Consideration of Way to Improve

    Ayako Yamagiwa, Masayuki Goto

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)   13316 LNCS   388 - 402  2022.06  [Refereed]

    Authorship:Last author

     View Summary

    Purchasing actions on e-commerce sites have become very common for general consumers in recent years. Products that were used to be bought at offline shops are purchased are also handled. Such products, like gifts or durable consumer goods, are often purchased infrequently and whose prefer items change each time they are purchased. A lot of methods are proposed for analysis purchase history data in order to improve customer satisfaction. However, most of them focus on the co-occurrence relationship between customers and products and treat products purchased by the same customer as similar. Then, it is difficult to use the conventional product analysis methods that have been proposed for purchase history data is difficult for some kinds of data mentioned before. Therefore, the authors have proposed an analysis method with extracting features of products by using the coefficients of binary classifiers that discriminates product purchases or not. In this study, we conduct experiments with artificial data in order to evaluate our method. Specifically, we verify how accurately the coefficients can be estimated and under what circumstances they can be estimated more accurately.

    DOI

    Scopus

  • Predicting Customer Churn for Platform Businesses: Using Latent Variables of Variational Autoencoder as Consumers’ Purchasing Behavior

    Kyosuke Hasumoto, Masayuki Goto

    Neural Computing and Applications   34 ( 21 ) 18525 - 18541  2022.06  [Refereed]

    Authorship:Last author

     View Summary

    Customer churn is considered a critical issue for all businesses, as customer loss leads to a decrease in future profits. Although acquiring new customers can address such losses, this process tends to cost more than retaining existing customers. Therefore, identifying potential churners and then retaining them is important. While churn prediction has been studied widely, current research must analyze more complex business models in response to their increase, such as platform businesses. However, modeling churn prediction for these businesses is challenging because consumer behavior over platforms is more complicated. Past approaches to churn prediction can be improved upon using recent advancements in deep learning that capture the nonlinear relationships behind the data in a data-driven manner, especially for complex business models. This study proposes a method of extracting latent features from purchase histories as explanatory variables for churn prediction using a variational autoencoder with the actual customer distribution as a prior. The proposed method is validated using real purchase data from a platform business and shows a 1.5% improvement in F-measure against the baseline, and a 20% improvement for customers with recent transactions. Subsequently, the variables are examined using several methods for data analysis to interpret the meanings of the extracted features and underlying customers’ purchasing behavior in the group of potential churners. With these analyses, the model provides practical implications for understanding what kind of purchasing behavior may lead to churn and planning effective retention strategies.

    DOI

    Scopus

    7
    Citation
    (Scopus)
  • Multi-task Learning Using Multi-layer Neural Networks to Predict Future Customer Behavior

    Kyosuke Hasumoto, Masayuki Goto

    IPSJ Journal   63 ( 6 ) 1276 - 1286  2022.06  [Refereed]

    Authorship:Last author

    DOI

  • An Analytical Model of Response Interval Between Employees on Business Chat Systems Based on Latent Class Model

    Fuyu Saito, Ayako Yamagiwa, Tianxiang Yang, Masayuki Goto

    Total Quality Science   7 ( 3 ) 149 - 160  2022.05  [Refereed]  [Invited]

    Authorship:Last author

    DOI

  • Analytical Model of Customer Purchasing Behavior Considering Event Characteristics on Flower Delivery Business

    Aya Kitasato, Kenya Nonaka, Haruka Yamashita, Masayuki Goto

    Total Quality Science   7 ( 3 ) 125 - 136  2022.05  [Refereed]  [Invited]

    Authorship:Last author

    DOI

  • 同一顧客の購入数が少ない商品群を対象とした購買履歴に基づく商品特性分析モデル

    山極 綾子, 雲居 玄道, 後藤 正幸

    電子情報通信学会論文誌 D   J105-D ( 5 ) 297 - 309  2022.05  [Refereed]

    Authorship:Last author

    DOI

  • Web Browsing History-based Attribute Labeling for Consumer Targeting

    Shogo Aoki, Kenta Mikawa, Masayuki Goto

    Journal of Japan Industrial Management Association   73 ( 1 ) 1 - 14  2022.04  [Refereed]

    Authorship:Last author

     View Summary

    In the recent world of diverse products, it is important to identify the consumer segments that should be targeted for each product. In order to identify the consumer segments to be targeted from among all consumers, it is common practice to use target attributes such as “male office workers in their 30s.” If data including attributes, hobbies and preferences of all consumers are available, target attributes can be identified using an analytical process. However, it is not realistic for each company to collect such data from all consumers for their products because of the huge cost involved. It is therefore, common to ask a consulting company that have the web browsing and purchasing histories of various sample consumers to analyze the sample data to identify the target. In particular, clustering behavioral data such as web browsing histories of sample consumers and discovering appropriate attributes that characterize target clusters (i.e. cluster attributes) is one of the most commonly used approaches. In real situations, cluster attributes are often assigned by the analyst based on the attribute statistics of the sample consumers belonging to the cluster, and multiple cluster attributes are often assumed for each cluster. When such qualitative analysis and judgment are involved, the selection of cluster attributes strongly depends on the experience and skills of the analyst. In this study, we formulate a model for clustering sample consumers based on their web browsing history, including various interests and preferences, and assigning effective cluster attributes as targets to each cluster. In addition, we propose a method to search for the best target attributes for a given objective function. By demonstrating an analysis of an actual data set, the effectiveness of the proposed method using real data is clarified.

    DOI

    Scopus

  • A Method for Network Construction Based on Communication Data on Business Chat Application

    Kenya Nonaka, Haruka Yamashita, Hotta Hajime, Masayuki Goto

    Transactions of the Japanese Society for Artificial Intelligence   37 ( 2 ) E - L63_1  2022.03  [Refereed]

    Authorship:Last author

     View Summary

    Visualizing social relationships by a network is useful for understanding the behavior of groups and individuals. The target of this study is a network between employees in the workplace. The construction of this network enables us to understand human relationships and managing a team. To build this network, the questionnaire and E-mail data were conventionally used. However, in this work, we use conversation history data on a chat application(Slack, etc.). We propose a method of quantifying the relationship between employees from conversation data on a chat application and visualizing it as a network between employees. Specifically, we assume that strongly related employees will make remarks at adjacent times on the chat, quantify the relationship by multivariate Hawkes process and build a network. To verify the effectiveness of the proposed model, we used Slack conversation data of a real company and extracted knowledge about team management from the network.

    DOI

    Scopus

  • An Explainable Recommendation Framework based on an Improved Knowledge Graph Attention Network with Massive Volumes of Side Information

    Ryotaro Shimizu, Megumi Matsutani, Masayuki Goto

    Knowledge-Based Systems   239   107970 - 107970  2022.03  [Refereed]

    Authorship:Last author

     View Summary

    In recent years, explainable recommendation has been a topic of active study. This is because the branch of the machine learning field related to methodologies is enabling human understanding of the reasons for the outputs of recommender systems. The realization of explainable recommendation is widely expected to increase both user satisfaction and the demand for explainable recommendation systems. Explainable recommendation utilizes a wealth of side information (such as sellers, brands, user ages and genders, and bookmark information, among others) to expound the decision-making reasoning applied by recommendation models. In explainable recommendation, although learning side information containing numerous variables leads to rich interpretability, learning too many variables presents a challenge because decreases the amount of learning that a given computational resource can perform, and the accuracy of the recommendation model may be degraded. However, numerous and diverse variables are included in the side information stored by the actual companies operating massive real-world services. Hence, to realize practical applications of this valuable information, it is necessary to resolve problems such as computational cost. In this study, we propose a new framework for explainable recommendation based on an improved knowledge graph attention network model, which utilizes the side information of items and realizes high recommendation accuracy. The proposed framework enables direct interpretation by visualizing the reasons for the recommendations provided. Experimental results show that the proposed framework reduced computational time requirements by approximately 80%, while maintaining recommendation accuracy by enabling the model to learn the probabilistically given edges included in the graph structure. Moreover, the results show that the proposed framework exhibited richer interpretability than the conventional model. Finally, a multifaceted analysis suggests that the proposed framework is not only effective as an explainable recommendation model but also provides a powerful tool for planning various marketing strategies.

    DOI

    Scopus

    42
    Citation
    (Scopus)
  • A Proposing Decision Model and Empirical Effect Verification of Sales Period with List Prices for Second-hand Fashion Items Based on Machine Learning Approach

    Izumi Kuwata, Kenta Mikawa, Hokuto Sasaki, Masayuki Goto

    IPSJ Journal   63 ( 1 ) 218 - 230  2022.01  [Refereed]

    Authorship:Last author

    DOI J-GLOBAL

  • Research Management with International Outcome Dissemination

    Masayuki Goto

      31 ( 2 ) 93 - 100  2022.01  [Invited]

    Authorship:Lead author, Last author, Corresponding author

  • A Study on Analysis Model of Customers’ Purchasing Behavior Based on Knowledge Graph Attention Network

    Fumiyo Ito, Zhiying Zhang, Gendo Kumoi, Masayuki Goto

      63 ( 1 ) 205 - 217  2022.01  [Refereed]

    Authorship:Last author

    DOI

  • A Study on New Product Recommendation Using Multi-Label CVAE for Fresh Flowers

    Aya Kitasato, Gendo Kumoi, Masayuki Goto

    2021 IEEE 12th International Workshop on Computational Intelligence and Applications (IWCIA 2021)    2021.11  [Refereed]

    Authorship:Last author

     View Summary

    In recent years, it has become very popular to use purchase history data on e-commerce sites for marketing measures to increase sales. Under such a situation, this paper considers measures using the purchase history data of a company providing delivering services of fresh flower products through an e-commerce site. This site deals mainly with fresh flowers, and the majority of items are purchased for gifts. The demands of flower gifts are usually strongly related with certain events, such as birthday, Mother's day, opening celebration, etc. Since each customer often makes purchase only at certain event when purchasing a flower gift, and it is important to encourage them to make purchases at other events from marketing viewpoint. In addition, the appearance of fresh flowers is important, so product recommendation with product images is necessary. It is relatively easy to develop floral gifts because they consist of certain patterns such as types of fresh flowers and shapes such as bouquets. However, there is no development of product which quantitatively uses purchase history information, The purpose of this research is, therefore, to generate product images that are preferred by customers in another event, considering the characteristics of product images purchased in individual event, where it is also possible to create new product images that are not contained in existing items. The proposed model is based on Conditional Variational Auto Encoader (CVAE) and can generate image outputs by inputting product images as multi-labels of events and attributes such as age and gender of customers that greatly affect product selection. Then, after learning a generator model, we consider to analyze what kinds of new products a customer with certain attributes who purchased at certain event would newly prefer at other events by changing the labels. Furthermore, in this study, we demonstrate the validity of the model by analyzing an actual data set.

    DOI

  • Time Window Topic Model for Analyzing Customer Browsing Behavior

    Fumiyo Ito, Gendo Kumoi, Masayuki Goto

    2021 IEEE 12th International Workshop on Computational Intelligence and Applications (IWCIA 2021)    2021.11  [Refereed]

    Authorship:Last author

     View Summary

    Nowadays, various services are available on the Internet, and a vast amount of website browsing history data is being accumulated. In recent years, even services with few purchase actions per user, such as booking a wedding venue or purchasing insurance, have become available on the Internet. For these services, it is assumed that the interests of users gradually change and narrow down during browsing. Then, when the users decide the product to purchase, it is considered that their interests converge on a specific subject. Therefore, it is important to implement appropriate marketing strategies depending on the degree of convergence of user interests to increase effectiveness. Therefore, a method that can analyze changing user interests over time from browsing history data is desired. In this study, we propose a Time Window Topic Model that can analyze changes in user interests by considering the interests as latent topics. The proposed method can reveal the changes in interests of users even in a real problem where it is difficult to apply conventional topic models. Finally, we verify the usefulness of the proposed method by analyzing an artificial dataset.

    DOI

  • Machine Learning for Demand Prediction of Seasonal Second-hand Fashion Items Based on Prior and Fine-tuning Prediction Models

    Fuyu Saito, Haruka Yamashita, Hokuto Sasaki, Masayuki Goto

    2021 IEEE 12th International Workshop on Computational Intelligence and Applications (IWCIA 2021)    2021.11  [Refereed]

    Authorship:Last author

     View Summary

    With the rapid development of information technology in recent years, it has become common for consumers to purchase various products via electric commerce (EC) sites. As a case study, this study focuses on ZOZOUSED, which is engaged in the business of buying used clothes from users, and reselling them as second-hand goods. From the perspective of inventory and management costs, it is desirable for items to be sold as soon as possible after they are listed on EC sites, and the number of listed items has been conventionally controlled, depending on the experience of item managers. However, owing to the subjective assessment of item managers, unnecessary price reductions for sales promotion of items, or opportunity losses triggered by an excessive number of listed items, may occur. For reasonable item management, the demand prediction for items by customers is a crucial task required to develop the optimal listing plan that balances supply and demand. Therefore, this study proposes a forecasting method of sales figures for the actual operation of listing second-hand goods, which comprises two-stage models: the first model is a prior seasonal long-term prediction of sales figures for each item group based on seasonal similarity, and the second model is a short-term fine-tuning prediction for daily operation via residual predictions with recent data. Furthermore, we apply the proposed model to the actual data of past sales figures accumulated in ZOZOUSED, and analyze the obtained results to demonstrate the usefulness of the proposed method. In addition, we empirically demonstrate the effectiveness of the proposed method by designing and performing an empirical experiment on an actual business by applying the output of the proposed method as a new index for determining the number of new items to be listed.

    DOI

  • Analysis of Entry Behavior of Students on Job Boards in Japan based on Factorization Machine Considering the Interaction among Features

    Tomoya Sugisaki, Yuri Nishio, Kenta Mikawa, Masayuki Goto, Takashi Sakurai

    Cogent Engineering   8 ( 1 )  2021.10  [Refereed]

    Authorship:Corresponding author

     View Summary

    Job-hunting activities in Japan are different from those in other countries. The features of this are the simultaneous recruitment of new graduates, joining the company in April, and the use by most students of such resources as employment information websites. In recent years, website job boards for new graduates have provided Japanese students with assistance in finding companies for which they want to work. On these boards, students can bookmark companies that they are interested in before deciding to apply. After bookmarking, a company bookmarked by a user can examine the information again later. However, even if the students rate various companies, many of these bookmarks do not lead to job applications. In other words, this can be regarded as a lost opportunity for gaining job applications from the perspective of the company. It is important for companies to gain as many job applications as possible to be successful in their recruitment activities. Therefore, a method of analyzing the entry behavior of students on job boards using factorization machines is proposed. The model predicts whether a student will submit a job application to a company. The prediction is based on student attributes and activity information, as well as information about the companies that they are interested in, as input variables. The interactions between input variables are also considered in making the prediction. In addition, the method supports student job-hunting activities and company measures for targeting students. To clarify the proposed model, analytical experiments were conducted with actual data from a website job board for new graduates.

    DOI

    Scopus

    1
    Citation
    (Scopus)
  • A Study on Recommender System Considering Diversity of Items based on LDA

    Zhiying Zhang, Taiju Hosaka, Haruka Yamashita, Masayuki Goto

    Asian Journal of Management Science and Applications   6 ( 1 ) 17 - 31  2021.10  [Refereed]

    Authorship:Last author

    DOI

  • An Analytical Model of Website Relationships based on Browsing History Embedding Considerations of Page Transitions

    Taiju Hosaka, Haruka Yamashita, Masayuki Goto

    Asian Journal of Management Science and Applications   6 ( 1 ) 1 - 16  2021.10  [Refereed]

    Authorship:Last author

    DOI

  • Feature Transfer Based Clustering for Designing Customers Growth Measures

    Yosuke Hirano, Tianxiang Yang, Gendo Kumoi, Haruka Abe, Tetsuya Tachibana, Masayuki Goto

    IPSJ Journal   62 ( 10 ) 1704 - 1715  2021.10  [Refereed]

    Authorship:Last author

    DOI

  • Deep Learning with Data Augmentation to Add Data Around Classification Boundaries

    Hideki Fujinami, Gendo Kumoi, Masayuki Goto

    Industrial Engineering & Management Systems   20 ( 3 ) 384 - 397  2021.09  [Refereed]

    Authorship:Last author

     View Summary

    Data augmentation methods are used as a technique to improve generalization by increasing the number of training data in image classification. However, most of these methods are not a data driven algorithm, the degree of improvement of generalization ability by performing these data augmentation methods differs between the domains of image data for training. Generative models are researched to use for augmenting data recently. In particular, Generative Adversarial Networks (GANs) (Goodfellow et al., 2014) that can generate clean image get attention as an excellent innovation in machine learning. As GANs extension method, there is a method called CGANs (Mirza and Osindero, 2014) that can be used for data augmentation. When enough training data for each class are not prepared for classification model, the same is true for training CGANs. In such case, CGAN generates noisy images. This makes a classification model to underfit to the original training data. Moreover, when a CGAN approximates the training data distribution, the CGAN generates new training data in the same region where training data densely exist. In such case, augmented data can't reduce overfitting on the original training data. Therefore, our research contributes to augment data which meets these two requirements. In this study, we propose a method to generate data by the class specific GAN with small training data and selectively add generated data to the training data set that improves classification accuracy by using the entropy of the classification model. The feature of the proposed method is that it focuses on the positional relationship between data and the classification hyperplane in deep learning. In the proposed method, the entropy of the classification model is used to measure the positional relationship between the classification boundary and the data. As a result, the generalization performance is improved by adding the data around the classification boundary as new training data.

    DOI

    Scopus

    2
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    (Scopus)
  • A Study on the Optimization of the ECOC Method for Multi-label Classification Problems

    Gendo Kumoi, Hideki Yagi, Manabu Kobayashi, Masayuki Goto, Shigeichi Hirasawa

      14 ( 3 ) 1 - 10  2021.08  [Refereed]

  • An Analytical Model of Exhibition Price Change Effects on Second-hand Fashion EC Site

    Shimpei Kanazawa, Tianxiang Yang, Masayuki Goto

    Journal of the Japan Society for Management Information   30 ( 1 ) 47 - 65  2021.06  [Refereed]

    Authorship:Last author

    DOI

  • A Model on Answering Documents Retrieval Considering Diversity Based on Topic Models

    Junya Okawa, Gendo Kumoi, Masayuki Goto

    Journal of the Japan Society for Management Information   30 ( 1 ) 31 - 46  2021.06  [Refereed]

    Authorship:Last author

     View Summary

    <p>A general QA system realizes automatic answering by analyzing questions given by the user based on a model that retrieves appropriate answer candidates for each question from a set of documents (an answering documents retrieval model). When constructing an answering documents retrieval model for question/answer documents for a community QA (Question Answering) sites (cQA sites), a method based on a similarity measure on question documents can be a basic method. However, it is difficult to present appropriate answering documents while accurately grasping the diversity of answer documents that exist for each question as seen on a cQA site. In this study, we propose a method for constructing an answering documents retrieval model that can consider the diversity of answer documents by using a topic model. In order to verify the effectiveness of a proposed model, a verification experiment using question/answer documents actually posted on a cQA site is performed.</p>

    DOI CiNii

  • A Latent Class Analysis for Item Demand Based on Temperature Difference and Store Characteristics

    Yuto Seko, Ryotaro Shimizu, Gendo Kumoi, Tomohiro Yoshikai, Masayuki Goto

    Industrial Engineering & Management Systems   20 ( 1 ) 35 - 47  2021.03  [Refereed]

    Authorship:Last author, Corresponding author

     View Summary

    In retail stores, there is an increasing need for predicting item demand using accumulated purchase history data to cope with the fluctuating consumer demands. These fluctuations in item demand are influenced by external factors and consumer preferences. Among these, store characteristics and weather conditions, which are closely related to consumer behavior, have strong effects on item demand. For this reason, it is very important to quantitatively grasp demand fluctuations of items that are influenced by changes in weather conditions for each store by using an integrated analysis of the purchase history data of many stores and weather conditions. In this research, we focus on the temperature difference, which is the average temperature difference from the previous day, as a weather condition affecting item sales. Because consumer feeling about a temperature is dependent on the temperature difference from the previous day, it is meaningful to construct a prediction model using this information. In this research, we propose a latent class model to express the relationship between weather conditions, store characteristics, and item demand fluctuation. Also, through an analysis experiment using an actual data set, we show the usefulness of the proposed model by extracting items that are influenced by weather conditions.

    DOI

    Scopus

    3
    Citation
    (Scopus)
  • A Study on Customer Purchase Behavior Analysis Based on Hidden Topic Markov Models

    Mio Hotoda, Gendo Kumoi, Masayuki Goto

    Industrial Engineering & Management Systems   20 ( 1 ) 48 - 60  2021.03  [Refereed]

    Authorship:Last author

     View Summary

    Along with recent developments of Internet society, purchasing actions on E-commerce (hereinafter called "EC") sites have become common for many consumers. On the other hand, it is known that the conversion rate (hereinafter called "CVR") on EC sites is usually several percent at most. Therefore, many EC sites desire effective measures to improve CVR. In general, a user browses several pages on an EC site before he/she decide to purchase an item and it is considered that users' intentions are reflected in their page transition tendency on an EC site. If a model analyzing the page transition data can extract users' purchasing intentions, it enables to utilize the information for making a good promotion measure. Here, it is sometimes better to assume latent classes behind the users' page transitions to understand their purchase intentions, because there are usually not only several user groups with different preferences but also plural states of purchasing intentions. However, previous models either assume the same latent topic on each page in the same session or assume a latent topic for each page every time. These models cannot handle situations where users' intentions may change during browsing but not change frequently from page to page. In this study, we propose a purchasing behavior analysis model based on Hidden Topic Markov Models (HTMM). The proposed method can divide users' browsing sequence into multiple subsequences with the same statistical characteristics according to latent topics estimated from page transitions. Then, the purchase probability of each latent topic can be obtained by using the purchase results obtained from the actual browsing history data together. By the proposed model, the purchase probabilities become possible to estimate the purchase intention of the users in real time and the information is effective for considering marketing measures. In this study, an experiment using real browsing history data is carried out and the effectiveness of the proposed method is demonstrated.

    DOI

    Scopus

    7
    Citation
    (Scopus)
  • An Extension of Semi-supervised Boosting to Multi-valued Classification Problems

    Yuta Sakai, Kazuki Yasui, Kenta Mikawa, Masayuki Goto

    Total Quality Sciene   6 ( 2 ) 60 - 69  2021.02  [Refereed]  [Invited]

    Authorship:Last author

    DOI

  • An Estimation Model of Exhibits Price for Second-hand Fashion Items Based on Sales History Data

    Izumi Kuwata, Tomoya Sugisaki, Kenta Mikawa, Masayuki Goto

    IPSJ Journal   62 ( 2 ) 796 - 808  2021.02  [Refereed]

    Authorship:Last author

    DOI J-GLOBAL

  • An Item Recommendation Algorithm on a Cyber Mall Based on TransRec Model Representing Stores by Linear Lines

    Yuichi Ohori, Tianxiang Yang, Haruka Yamashita, Masayuki Goto

    IPSJ Journal   62 ( 2 ) 782 - 795  2021.02  [Refereed]

    Authorship:Last author

    DOI J-GLOBAL

  • Factorization Machines Considering the Latent Characteristics behind Target Data

    Tomoya Sugisaki, Kenta Mikawa, Masayuki Goto

    Asian Journal of Management Science and Applications   5 ( 2 ) 111 - 128  2021.01  [Refereed]

    Authorship:Last author

    DOI

  • A Proposal of Trend Analysis Method for TV Viewing Data based on Topic Model

    Teppei Sakamoto, Yusuke Kobayashi, Keiichiro Nakagawa, Takashi Namatame, Masayuki Goto

    IPSJ Journal   62 ( 1 ) 346 - 356  2021.01  [Refereed]

    Authorship:Last author

    DOI

  • Customer Behaviour Analysis Based on Buying-data Sparsity for Multi-category Products in Pork Industry: A Hybrid Approach

    Arthit Apichottanalul, Masayuki Goto, Kullaprapruk Piewthongngam, Supachai Pathumnakul

    Cogent Engineering   8 ( 1 )  2021.01  [Refereed]

     View Summary

    Understanding customer behaviour is crucial for business success. For achieving this goal, the Recency–Frequency–Monetary (RFM) model has been commonly recognised as an effective approach to analyse customer behaviour. However, the traditional RFM approach is a coarse method for quantifying customer loyalty and contribution that can only provide a single lump-sum value of the recency (R), frequency (F), and monetary value (M); hence, it discards information regarding customers’ product preferences. Typically, different customers make different purchases. Subsequently, purchases are likely to be different across customers. This creates data sparsity, which affects the performance of conventional clustering methods. In this study, we integrated the group RFM analysis and probabilistic latent semantic analysis models to perform customer segmentation and customer analysis. The results indicated that the developed approach takes into account the product preference and provides insight into and captures a wide variety of the types of true ordering behaviour of the company’s customers. The information allows the manager to improve customer relationships and build a personalised purchasing management system for grouping customers with similar purchasing patterns.

    DOI

    Scopus

    7
    Citation
    (Scopus)
  • A Hypothesis Discovery Method for Predicting Change in Multidimensional Time-Series Data

    Gendo Kumoi, Masayuki Goto

    IEEE International Conference on Systems, Man and Cybernetics   2020-October ( SMC2020 ) MoBT13.3 - 859  2020.11  [Refereed]

    Authorship:Last author

     View Summary

    With the development of IoT technology, it has become possible to accumulate and regularly measure multidimensional time-series data. In this study, we focus on the usage of multidimensional time-series data from printer products' log data and propose a method for its analysis. In addition to the number of sheets printed by each customer, the log data includes various time-series information such as the amount of remaining toner, the number of stoppages that occur, and the activation times. To utilize these data for business purposes, it is desirable to construct a model for predicting future changes in use characteristics for each customer. In this study, we apply the random forest algorithm to predict such changes. However, if all measurable features of the problem are included, the model becomes complex and cannot be interpreted. Although the accuracy is relatively high if an appropriate learning algorithm is applied, the complex model tends to overfit the training data. In this paper, we propose a method to select the modeling features that can be interpreted by graph mining while maintaining accuracy. This would enable us to interpret the data at the field level and discover the hypotheses that are necessary for planned marketing policies. Finally, the proposed method is applied to real data and its efficacy is demonstrated.

    DOI

    Scopus

  • Model for Relational Analysis of Posted Articles and Reactions on Restaurant Guide Sites

    Teppei Sakamoto, Haruka Yamashita, Masayuki Goto, Jiro Iwanaga

    Industrial Engineering & Management Systems   19 ( 3 ) 669 - 679  2020.09  [Refereed]

    Authorship:Last author

     View Summary

    Recently, restaurant guide sites providing restaurant information posted by users on the Internet have been widely used as effective tools for consumers. Users, on a restaurant guide site, utilize IDs to post their recommendation articles on restaurants, and these posted articles are a valuable information source for other users. Open users can search for restaurants and read recommendation articles posted by other users. Furthermore, they can react (e.g., “like”) to a recommendation article when they feel it is helpful or they feel like visiting the restaurant. On a target restaurant guide site, each post includes the user ID, restaurant name, recommendation sentences, etc., and the number of reactions is considered to depend on these posted contents. For users who post recommendation articles, the number of reactions to their posts represents the degree of empathy from other users and is an important motivation for posting. Therefore, posting users will benefit from guidelines on how to write good recommendation sentences to increase the number of reactions. Moreover, the number of reactions can be regarded as an important indicator of the activity level of the restaurant guide site from the viewpoint of the service operating company. Therefore, an analytical model developed using historical information such as posts and reactions by users would be useful for determining the relationship between posted contents and the number of reactions. Therefore, this paper proposes a model based on the machine learning approach to analyze the relation between the number of reactions and posted contents. Finally, we demonstrate the analysis based on the proposed model using practical data.

    DOI

    Scopus

    1
    Citation
    (Scopus)
  • Analysis of purchase history data based on a new latent class model for RFM analysis

    Qian Zhang, Haruka Yamashita, Kenta Mikawa, Masayuki Goto

    Industrial Engineering and Management Systems   19 ( 2 ) 476 - 483  2020.06  [Refereed]

    Authorship:Last author

     View Summary

    Recently, it has become easier to make use of various kinds of information on customers (e.g. customers' purchase history), due to the development of information technology. Especially in the marketing field, in fact, many companies try to employ customer segmentation for the services customization which leads to increase customer loyalty and to keep high customer retention. One of the well-known approaches for the customer analysis based on purchase history data is the RFM analysis. The RFM analysis is usually used to segment customers into several groups by using three variables; how long it has been since their last purchase, how many times they purchased, and how much they spent. However, the conventional method of the RFM analysis did not assume a generative model. Therefore, when applying to an actual data set and scoring each index of R, F, M scores, several problems occur. The main problem is that an analyst should arbitrarily decide the threshold for the scores of RFM. On the other hand, in the field of machine learning, the probabilistic latent semantic analysis is widely used for soft clustering. The latent class model enables us to cluster customers into latent classes and to calculate the assignment probabilities of each customer to each latent class. In this paper, we propose a new latent class model for the RFM analysis based on the purchase history data. The proposed model enables to decide the scoring of RFM and segment customers automatically, and the soft clustering approach helps the interpretation of the result. Furthermore, the proposed model takes account of the generation model of RFM scores. From the result of actual data analysis, it became clear that it is possible to extract latent classes that express the statistical characteristics of data well. Given a generative model estimated from the given data, it is also possible to predict future purchase behaviors of customers or to generate virtual data for simulation analysis and make decisions based on the result. We verify the effectiveness of our model by analyzing a real purchase history data of a Japanese major retail company.

    DOI

    Scopus

    7
    Citation
    (Scopus)
  • Relationship Analysis of Query and Answer Documents Based on Latent Dirichlet Allocation and Its Application

    Junya Okawa, Gendo Kumoi, Masayuki Goto

    Journal of the Japan Society for Management Information   29 ( 1 ) 476 - 483  2020.06  [Refereed]

    Authorship:Last author

    DOI

  • Technical Trends of Data Analytics on Data-driven Approaches

    Masayuki Goto

      103 ( 5 ) 461 - 467  2020.05  [Invited]

    Authorship:Lead author, Corresponding author

  • An Investigation into Attitudes toward Physically Challenged Persons in Nepal: A Comparative Study with Japan

    Fuyu Saito, Haruka Yamashita, Manita Shrestha, Masayuki Goto

    Environment and Ecology Research   8 ( 2 ) 29 - 40  2020.04  [Refereed]

    Authorship:Last author, Corresponding author

     View Summary

    There are serious problems with the increase of challenged persons in developing countries and we have to consider how to help them. Moreover, we also have to ask whether the Quality of Life (QOL) for them has actually improved. Do they feel despair over their real ambitions and are they content to be patient while in a state of unrealized potential? In other words, we have to have awareness not only how to deal with people who need help to live, but also how to make a society in which everyone can live independently and freely. In this research, we investigate the attitudes of Nepalese citizens who don't need a great support for living towards physically challenged persons and identify the problems with the current attitude of citizens towards them and discuss possible approaches and a vision for improving their QOL. To this end, we conducted a field survey in Nepal and Japan using a questionnaire, analyzed the data, and discussed to clarify the Nepalese awareness of physically challenged persons. In the end, we consider a possible solution to these problems from several viewpoints. By collecting data by various stratifications, it was found that there was a large difference in their awareness toward challenged persons by ethnic group, age, region, and gender as the major findings of the surveys. Also the Nepalese themselves are aware of the difference. Based on the above results, we point out that “unifying actual support and promoting citizens’ awareness,” “reviewing and strengthening the educational system,” and “strengthening the leadership of the government to eliminate the differences between countries, genders, and caste groups” are three ways to address this issue. These three issues will have a positive effect on challenged persons in Nepal and also have a positive effect on society by reducing the burden on supporters.

    DOI

    Scopus

    1
    Citation
    (Scopus)
  • Binary Codeword Table for Multilevel Document Classification Using Information Theoretic Criterion

    Gendo Kumoi, Hideki Yagi, Masayuki Goto, Shigeichi Hirasawa

    IPSJ TOM Journal   13 ( 1 ) 1 - 12  2020.03  [Refereed]

    Authorship:Corresponding author

  • Latent Variable Models for Integrated Analysis of Credit and Point Usage History Data on Rewards Credit Card System

    Ryotaro Shimizu, Haruka Yamashita, Masao Ueda, Ranna Tanaka, Tetsuya Tachibana, Masayuki Goto

    International Business Research   13 ( 3 ) 106 - 117  2020.02  [Refereed]

    Authorship:Last author, Corresponding author

    DOI

  • Development of Debugging Exercise Extraction System using Learning History

    Katsuyuki Umezawa, Makoto Nakazawa, Masayuki Goto, Shigeichi Hirasawa

    Proceedings - IEEE 10th International Conference on Technology for Education, T4E 2019     244 - 245  2019.12

     View Summary

    We have proposed an editing history visualization system which can confirm where and how the learner modified program. We utilized this system for actual flipped classroom and stored a large amount of learning logs. This learning log contains all the source code in the process of being modified until the program is completed. We developed a debugging exercise extraction system to automatically generate problems for debugging practice from this learning log. The debugging exercise extraction tool we developed extracted 18,680 source codes (which became practice problems) that included syntactic errors that could be used as a debugging exercise from 16 weeks of program edit history data (total number is 31,562 files). The execution time was 488 seconds. Since it can be analyzed only once every six months, we believe it is a sufficiently practical execution time.

    DOI

    Scopus

    5
    Citation
    (Scopus)
  • Relational Analysis Model of Weather Conditions and Sales Patterns Based on Nonnegative Tensor Factorization

    Sei Okayama, Haruka Yamashita, Kenta Mikawa, Masayuki Goto, Tomohiro Yoshikai

    International Journal of Production Research   58 ( 8 ) 2477 - 2489  2019.12  [Refereed]

    Authorship:Corresponding author

     View Summary

    It is necessary to analyze the relationships between the retail sales of various items and weather conditions. However, the relationship between the sales of each item and the weather condition may vary among stores. Additionally, it is necessary to model the statistical relationships between a wide variety of goods and weather conditions by using past sales data. In such a case, it becomes unrealistic to construct a forecast model for every individual item owing to the breadth of items and the number of retail shops. This study proposes a model to analyze the relationships between the sales of various items and weather conditions. This method can be used to decompose the data into three matrices based on the nonnegative tensor factorization (NTF) method. The results of the analysis clarified that the proposed model can identify important items whose demand is strongly influenced by weather conditions, thereby increasing the effectiveness of inventory management. Additionally, the store clusters estimated by the proposed model can facilitate the construction of regression models that demonstrate the relationship between the sales of each item and weather conditions.

    DOI

    Scopus

    2
    Citation
    (Scopus)
  • ユーザの行動履歴データを活用したネットワーク分析

    Masayuki Goto

    Operations Research   64 ( 11 ) 671 - 678  2019.11  [Invited]

  • Analytical Model of Customer Purchase Behavior Considering Page Transitions on EC Site

    Mio Hotoda, Hiroki Mizuochi, Gendo Kumoi, Masayuki Goto

    Total Quality Science   5 ( 1 ) 23 - 33  2019.10  [Refereed]  [Invited]

    Authorship:Last author

    DOI

  • A Study of Feature Clustering Analysis based on the Hidden Layer Representation of an Autoencoder

    Shimpei Kanazawa, Yuuki Sugiyama, Tianxiang Yang, Masayuki Goto

    Total Quality Science   5 ( 1 ) 11 - 22  2019.10  [Refereed]  [Invited]

    Authorship:Last author

    DOI

  • System Evaluation of Ternary Error-Correcting Output Codes for Multiclass Classification Problems*

    Shigeichi Hirasawa, Gendo Kumoi, Hideki Yagi, Manabu Kobayashi, Masayuki Goto, Tetsuya Sakai, Hiroshige Inazumi

    2019 IEEE International Conference on Systems, Man and Cybernetics (SMC)   2019-October   2893 - 2898  2019.10  [Refereed]

     View Summary

    To solve multiple classification problems with M (geq 3) categories, many studies have been devoted using N (geq lceillog-{2}Mrceil) binary ({0, 1}) classifiers, where these systems are known as binary Error-Correcting Output Codes (binary ECOC). As an extended version of the binary ECOC, the ternary ({0,∗1}) ECOC have also been discussed, where ternary classifiers classify data into positive examples when the element is 1, into negative examples when the element is 0, and no classification when the element is. In this paper, we discuss the ternary ECOC system from the view point of the system evaluation model based on rate-distortion function. First, we discuss a table of M code words with length N which is given by a ternary matrix W of M rows and N columns. Next, by leveraging the benchmark data for multiclass document classification which is widely used in Japan, the relationships between the probability of classification error Pe and the number of the ternary classifiers N for a given M are experimentally investigated. In addition, by assuming the M-dimensional Normal distribution for a classification data model, the relationship between Pe and N for a given M is also examined. Finally, we show by the system evaluation model that the ternary ECOC systems have desirable properties such as 'Flexible', 'Elastic', and 'Effective Elastic', when M becomes large.

    DOI

  • An Analytic Model to Represent Relation between Finish Date of Job-Hunting and Time-Series Variation of Entry Tendencies

    Seiya Nagamori, Kenta Mikawa, Masayuki Goto, Tairiku Ogihara

    Industrial Engineering & Management Systems   18 ( 3 ) 292 - 304  2019.09  [Refereed]

    Authorship:Corresponding author

     View Summary

    Currently, most university students in Japan use Internet portal sites for job-hunting activities. However, job-hunting activities are sometimes prolonged owing to a mismatch between a student and the company requirements. To solve this problem, it is important to find the students who may not be able to finish job-hunting early; this goal can be achieved by utilizing user behavior log data stored on an Internet portal site. This study proposes appropriate statistical model based on a latent class model. Specifically, we also apply clustering approach and takes account of time-series variation. The proposed model enables us to analyze entry patterns from the viewpoint of time-series variation of job-hunting activities and to predict the finish date of job-hunting for each cluster. Through the simulation experiments, the effectiveness of the proposed method was clarified. We used actual data of students' activities from an Internet portal site to demonstrate the effectiveness of the proposed method that considers the time series of the entry tendency of student users. By considering the time shift of students' preferences, it became possible to extract students who tend to struggle in job-hunting activities. It is possible to specify students who should be supported by using the proposed model.

    DOI

    Scopus

    1
    Citation
    (Scopus)
  • A Consideration on Consumer's Purchasing Behavior Analysis Model Focusing on Periodicity and Event Effect

    Kazuki Yasui, Shuhei Nakano, Kenta Mikawa, Masayuki Goto

    Journal of the Japan Society for Management Information   28 ( 2 ) 69 - 87  2019.09  [Refereed]

    Authorship:Last author

    CiNii

  • A Model for Integrated Analysis of Website and User by Gaussian Embedding

    Taijyu Hosaka, Ryota Kawabe, Haruka Yamashita, Masayuki Goto

    IPSJ Journal   60 ( 8 ) 1390 - 1402  2019.08  [Refereed]

    Authorship:Last author

  • A New Analytical Model for Customer Growth Considering Potential Purchasing Preferences

    Yuri Nishio, Hiroaki Ito, Haruka Yamashita, Masayuki Goto

    Total Quality Science   4 ( 3 ) 148 - 159  2019.07  [Refereed]  [Invited]

    Authorship:Last author

    DOI

  • An Analytical Model of Relation between Browsing and Entry Activities on an Internet Portal Site for Job-hunting

    Yuuki Sugiyama, Takumi Arai, Tianxiang Yang, Tairiku Ogihara, Masayuki Goto

    Total Quality Science   4 ( 3 ) 109 - 118  2019.07  [Refereed]  [Invited]

    Authorship:Last author

    DOI

  • Hierarchical Structure Learning in a Bayesian Network for the Analysis of Purchasing Behavior

    Ryota Kawabe, Hiroaki Ito, Haruka Yamashita, Masayuki Goto

    Total Quality Science   4 ( 3 ) 99 - 108  2019.07  [Refereed]  [Invited]

    Authorship:Last author

    DOI

  • Binary Document Classification Based on Fast Flux Discriminant with Similarity Measure on Word Set

    Keisuke Okubo, Gendo Kumoi, Masayuki Goto

    Industrial Engineering & Management Systems   18 ( 2 ) 235 - 241  2019.07  [Refereed]

    Authorship:Last author

     View Summary

    Fast Flux Discriminant (FFD) is known as one of the high-performance nonlinear binary classifiers, and it is possible to construct a classification model considering the interaction between variables. In order to take account of the interaction between variables, FFD introduces the histogram-based kernel smoothing using subspaces including variable combinations. However, when creating a subspace, the original FFD should cover all variables including combinations of variables with low interaction. Therefore, the disadvantage is that the calculation amount increases exponentially as the dimension increases. In this study, we calculate the similarity between variables by using KL divergence. Then, among the obtained similarities, divisions are performed for each subspace with similar variables. Through this method, we try to reduce the amount of calculation while maintaining classification accuracy by using only combinations of variables that are likely to take high interaction. Through the simulation experiments with Japanese newspaper articles, the effectiveness of our proposed method is clarified.

    DOI

    Scopus

  • 非負値行列因子分解を用いたプラットフォームビジネスにおける顧客生涯価値予測

    Kyosuke Hasumoto, Gendo Kumoi, Masayuki Goto

    IPSJ Journal   60 ( 7 ) 1283 - 1293  2019.07  [Refereed]

    Authorship:Last author

  • Latent Class Models on Business Analytics

    Masayuki Goto

    2019 IEEE International Conference on Big Data, Cloud Computing, and Data Science     142 - 147  2019.05  [Refereed]

    Authorship:Lead author, Last author, Corresponding author

     View Summary

    This paper discusses the systematic application of the latent class model on business analytics. The latent class model is one of the effective statistical model classes on business analytics to represent essential statistical structures by learning the sparse and high dimensional data. This model class is useful for the purpose of reduction of feature dimension and cancellation of sparseness of the data. This is because many practical data can be assumed to consist of several unobserved subgroups. For example, a customers set, which is a target in the field of marketing analysis, consists of several subgroups with different characteristics and preferences. In addition, data clustering can also be realized by estimated belonging probabilities to latent classes of each data.This paper gives a general form of the latent class model and discuss how to construct the model structure and apply to a real problem in business analytics. After describing important points to be noted in analysis based on a latent class model, several practical examples are also shown.

    DOI

    Scopus

    3
    Citation
    (Scopus)
  • Greedy Features Quantity Selection Method from Multivariate Time Series Data for Customer Classification

    Gendo Kumoi, Masayuki Goto

    2019 IEEE International Conference on Big Data, Cloud Computing, and Data Science (BCD 2019)     154 - 159  2019.05  [Refereed]

    Authorship:Last author

     View Summary

    In recent years, toward the realization of the Internet of Things ('IoT') society, related technologies have been developed and various electronic devices are being connected to the network. Even in companies that provide such kinds of products and services, it is possible to collect usage histories of their customers. If the companies can appropriately analyze the usage history data, it is useful for their marketing activities. However, in general, device usage histories are multivariate time-series data, and it is not obvious how to construct a feature space for customer classification and clustering. Therefore, this paper proposes a method to automatically select feature quantities characterizing the properties of customers using machine learning. We apply this method to real data and show its effectiveness.

    DOI

    Scopus

  • A Study on Analysis Methods of Latent Customer Purchase Behavior Focused on Membership Stage Growth

    Tianxiang Yang, Haruka Yamashita, Masayuki Goto

    2019 IEEE International Conference on Big Data, Cloud Computing, and Data Science (BCD 2019)     148 - 153  2019.05  [Refereed]

    Authorship:Last author

     View Summary

    With the development of information technology, it has become possible to collect large amounts of customer related data. In particular, retail companies have demands to extract target customers by analyzing the accumulated purchase behavior data of customers. They also need to clarify the impact of items that is related to the good customers. Therefore, a large number of retail companies use their business data for marketing activities. In this study, we focus on a company that make use of a membership-stage system. The membership-stage system sets customer ranks, which we call customer stages, based on the annual spending of each customer. Each customer is given privileges such as discounts depending on their rank. As the customer's rank move up, the customers' purchase behavior varies widely. Therefore, finding the items that lead to for customer's stage growth at each stage becomes an important goal in marketing analysis. For example, there is a framework using a linear classifier to define the importance of items between customers who belong in different stages by Yang et al.. They apply Support Vector Machine(SVM) to the purchase data of customers, classifying whether or not the customers will move up a stage in a year. During the training, moving up a stage is set positive data, and the others are set negative data. Then, they interpret the importance of items using the coefficients of the discrimination function obtained by SVM for each stage. However, the proposal of Yang et al. didn't solve the following two problems. The first problem is that the importance of items among different preferences at each stage could not be interpreted from the results. The second problem is that the features of customers that become a good customer in the first three months of the year will differ from the features of customers in the next nine month, making it difficult to devise effective feature measures. In this study, we propose an analysis method to uncover the impact of latent customer purchase behavior towards stage promotion. We only use three months of customers' purchase data to predict whether the user is capable of becoming a good customer or not using Random Forest. Here, we apply the the PLSA's results of users' purchase behavior for the explanatory variable of Random Forest and interpret the important features in of classes between good customers and the other. Moreover, we apply the proposed method to actual purchase history data to verity the performance of our proposal.

    DOI

    Scopus

    3
    Citation
    (Scopus)
  • Selling Price Prediction Model Construction of Second-hand Fashion Items Based on Sales History Data

    Masato Ninohira, Kenta Mikawa, Masayuki Goto

    IPSJ Journal   60 ( 4 ) 1151 - 1161  2019.04  [Refereed]

    Authorship:Last author

     View Summary

    Recently, it has become popular for consumers to purchase product items through EC sites. Especially as fashion items, the purchasing actions by consumers for them through EC sites have been rapidly increased. This study focuses on a fashion EC site which operates the resale business of second-hand clothes. They assess the appropriate exhibit prices of second-hand fashion items and resell them on this EC site. A characteristic of this EC site is that if an item is not bought for a certain period, the price force to be discounted automatically. In this EC site, it is important to predict the selling price of each item in condition given information and an exhibit price. When we can predict accurate selling price and clear the effects of factors on selling price, it should help a various marketing strategies. In this paper, we propose a new regression model to predict selling price using linear regression models depending on clusters which are constructed by the relation between the features of items and seasonal off-rate. In order to show the effectiveness of our proposal, simulation experiments with a real data are demonstrated and we discuss the analysis of the results for some insightful marketing policies.

    CiNii

  • Customer Clustering Based on a Latent Class Model Representing Preferences for Item Seasonality

    Masato Ninohira, Haruka Yamashita, Masayuki Goto

    Journal of Japan Industrial Management Association   69 ( 4E ) 195 - 206  2019.02  [Refereed]

    Authorship:Last author

     View Summary

    It has recently become easier for retail stores to obtain mass customer purchase history data. Analyzing these data, it is possible to understand the preferences of each customer and to use the results for marketing strategies. At the same time, it is important to take into account item seasonality in supermarkets planing marketing policies. It is, therefore, necessary to understand whether each customer purchases items based on seasonality throughout the year. In this study, we propose a new latent class model for analyzing customers’ purchasing behavior focusing on the seasonality of items, and demonstrate an analysis using our model. Moreover, we show that analysis of customers’ purchase behavior using both conventional latent class models and our latent class model provides more useful results than using only one model.

    DOI

    Scopus

    2
    Citation
    (Scopus)
  • How Did the 2015 Political Crisis Affect Nepal in Economic and Social Respects?

    Ryotaro Shimizu, Brenda Bushell, Masayuki Goto

    Environment and Ecology Research   6 ( 6 ) 571 - 582  2018.12  [Refereed]

    Authorship:Last author

    DOI

  • Proposal of a Purchase Behavior Analysis Model on an Electronic Commerce Site Using Questionnaire Data

    Ryotaro Shimizu, Teppei Sakamoto, Haruka Yamashita, Masayuki Goto

    Total Quality Science   4 ( 1 ) 1 - 12  2018.10  [Refereed]  [Invited]

    Authorship:Last author

    DOI

  • A Visualization System of the Contribution of Learners in Software Development PBL Using GitHub

    Yutsuki Miyashita, Atsuo Hazeyama, Hiroaki Hashiura, Masayuki Goto, Shigeichi Hirasawa

    Proceedings - Asia-Pacific Software Engineering Conference, APSEC   2018-December   695 - 696  2018.07

     View Summary

    In recent years, the paradigm of social coding in software development has attracted attention to developers all over the world, and GitHub which is a social coding tool has spread to the area like education. There are many cases using it as a platform of PBL (Project Based Learning). However, since GitHub is not a tool for education, it is difficult to evaluate learners. This research focuses on the contribution of learners and proposes a system that teachers can grasp the contribution of learners.

    DOI

    Scopus

  • Quantification of Sensible Temperature by Analyzing Meteorological Information and Tweet Data and Its Application to Demand Forecasting

    MAGA, Takashi, MIKAWA Kenta, GOTO, Masayuki, YOSHIKAI, Tomohiro

    電子情報通信学会論文誌D   J101-D ( 7 ) 1037 - 1051  2018.07  [Refereed]

  • Least Square Error Estimation for Regression Models with Mixed Error Distribution

    Masayuki Goto, Manabu Kobayashi, Shigeichi Hirasawa

    International Conference of Engineering, Technology, and Applied Science   ( ICETA 2018 ) 123 - 129  2018.06  [Refereed]

    Authorship:Lead author, Corresponding author

  • A Greedy Construction Approach of Codeword Table on Error Correcting Output Coding for Multivalued Classification and Its Evaluation by Using Artificial Data

    Gendo Kumoi, Hideki Yagi, Manabu Kobayashi, Masayuki Goto, Shigeichi Hirasawa

    International Conference of Engineering, Technology, and Applied Science   ( ICETA 2018 ) 15 - 22  2018.06  [Refereed]

  • System Evaluation of Error Correcting Output Codes for Artificial Data Methods

    Shigeichi Hirasawa, Gendo Kumoi, Manabu Kobayashi, Masayuki Goto, Hiroshige Inazumi

    International Conference of Engineering, Technology, and Applied Science   ( ICETA 2018 ) 112 - 122  2018.06  [Refereed]

  • Recommendation System Based on Unexpectedness Index that Balances Estimated Purchase Probabilities and Predicted Evaluation Values

    Ayumi Sekiguchi, Masato Ninohira, Kenta Mikawa, Masayuki Goto

    経営情報学会誌   27 ( 1 ) 67 - 78  2018.06  [Refereed]

    Authorship:Last author

    CiNii

  • System Evaluation of Construction Methods for Multi-class Problems Using Binary Classifiers

    Shigeichi Hirasawa, Gendo Kumoi, Manabu Kobayashi, Masayuki Goto, Hiroshige Inazumi

    Advances in Intelligent Systems and Computing   746   909 - 919  2018.05  [Refereed]

     View Summary

    Construction methods for multi-valued classification (multi-class) systems using binary classifiers are discussed and evaluated by a trade-off model for system evaluation based on rate-distortion theory. Suppose the multi-class systems consisted of M(≥3) categories and N(≥M-1) binary classifiers, then they can be represented by a matrix W, where the matrix W is given by a table of M code words with length N, called a code word table. For a document classification task, the relationship between the probability of classification error Pe and the number of binary classifiers N for given M is investigated, and we show that our constructed systems satisfy desirable properties such as “Flexible”, and “Elastic”. In particular, modified Reed Muller codes perform well: they are shown to be “Effective elastic”. As a second application we consider a hand-written character recognition task, and we show that the desirable properties are also satisfied.

    DOI

    Scopus

    2
    Citation
    (Scopus)
  • A Study of Analysis Model of Number of Students' Applications on Internet Portal Site for Job-hunting Based on Mixture Regression

    NAGAMORI, Seiya, YAMASHITA, Haruka, OGIHARA, Tairiku, GOTO, Masayuki

    IPSJ Journal   59 ( 4 ) 1273 - 1285  2018.04  [Refereed]

    Authorship:Last author

  • An Analysis Model Based on Latent Class Models to Increase Reactions to Restaurant Recommendation on Social Gourmet Service

    LIU, Peijie, YAMASHITA, Haruka, IWANAGA, Jiro, TARUISHI, Masato, GOTO, Masayuki

    IPSJ Journal   59 ( 1 ) 211 - 226  2018.01  [Refereed]

    Authorship:Last author

  • Collaborative filtering based on the latent class model for attributes

    Manabu Kobayashi, Kenta Mikawa, Masayuki Goto, Toshiyasu Matsushima, Shigeichi Hirasawa

    Proceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017   2017-December ( ICMLA2017 ) 893 - 896  2017.12  [Refereed]

     View Summary

    In this manuscript, we investigate a collaborative filtering method to characterize consumption behavior of customers and services with various attributes for marketing. We assume that each customer and service have the invisible attribute which is called latent class. Assuming a combination of attribute values of a customer and service is classified to a latent class, furthermore, we propose a new Bayesian statistical model that consumption behavior is probabilistically arise based on a latent class combination of a customer, service and attribute values. Then, we show the method to estimate parameters of a statistical model based on the variational Bayes method and the mean field approximation. Consequently, we show the effectiveness of the proposed model and the estimation method by simulation.

    DOI

    Scopus

  • Letent Semantic Markov Model for Effective Promotion Activities in EC Sites

    MATSUZAKI, Yuki, MIKAWA, Kenta, GOTO, Masayuki

    IPSJ Journal   58 ( 12 ) 2034 - 2045  2017.12  [Refereed]

    Authorship:Last author

     View Summary

    Recently, it has become popular to purchase product items through E-commerce sites (EC sites), and the internet market scale has been expanding. Under this situation, many EC sites conduct various kinds of sales promotions by analyzing huge amount of customers' purchase histories, and customer segmentation is one of the most important tools in marketing. Particularly, modeling of customers purchase behavior based on probabilistic models such as the Aspect Model (AM) is an attractive way for customer segmentation. The AM focuses on pairs of a customer and an item and it assumes unobserved features such as customers' heterogeneity and items' similarity as latent classes. Although the original AM focuses on pairs of a customer and an item mainly, the data about customers' browsing histories are also available on EC sites. If the model can take in the information of page transitions, it becomes possible to model customers' purchase behavior in detail and make better customer segments. In this paper, we propose a new latent class model that integrates browsing histories in addition to purchase histories by assuming that customers' page transitions can be described by Markov process. An analysis of actual EC site data is demonstrated to clarify the effectiveness.

    CiNii

  • A Study of ECOC Multi-category Classification Approach Based on Code Table Considering Binary Classification in Same Category

    SUZUKI, Leona, YAMASHITA, Haruka, GOTO, Masayuki

    IPSJ Journal   58 ( 12 ) 2046 - 2059  2017.12  [Refereed]

    Authorship:Last author

  • Distance Metric Learning using Each Category Centroid with Nuclear Norm Regularization

    Kenta Mikawa, Manabu Kobayashi, Masayuki Goto, Shigeichi Hirasawa

    IEEE Symposium Series on Computational Intelligence   ( IEEE SSCI 2017 ) SS-1246 - 5  2017.11  [Refereed]

    DOI

  • Collaborative Filtering Analysis of Consumption Behavior Based on the Latent Class Model

    Manabu Kobayashi, Kenta Mikawa, Masayuki Goto, Shigeichi Hirasawa

    IEEE International Conference on Systems, Man, and Cybernetics   2017-January ( IEEE SMC2017 ) 1926 - 1931  2017.10  [Refereed]

     View Summary

    In this manuscript, we investigate a collaborative filtering method to characterize consumption behavior (or evaluation) of customers (or users) and services (or items) for marketing. Assuming that each customer and service have the invisible attribute, which is called latent class, we propose a new Bayesian statistical model that consumption behavior is probabilistically arise based on a latent class combination of a customer and service. Then, we show the method to estimate parameters of a statistical model based on the variational Bayes method and the mean field approximation. Consequently, we show the effectiveness of the proposed model and the estimation method by simulation and analyzing actual data.

    DOI

    Scopus

  • A Survey on Present Tourism in Nepal and Its Ripple Effects on Other Industries

    Takumi Arai, Masayuki Goto

    Environment and Ecology Research   5 ( 7 ) 467 - 475  2017.10  [Refereed]

    Authorship:Last author

    DOI

  • Adaptive Prediction Method Based on Alternating Decision Forests with Considerations for Generalization Ability

    Shotaro Misawa, Kenta Mikawa, Masayuki Goto

    Industrial Engineering & Management Systems   16 ( 3 ) 384 - 391  2017.10  [Refereed]

    Authorship:Last author

     View Summary

    Many machine learning algorithms have been proposed and applied to a wide range of prediction problems in the field of industrial management. Lately, the amount of data is increasing and machine learning algorithms with low computational costs and efficient ensemble methods are needed. Alternating Decision Forest (ADF) is an efficient ensemble method known for its high performance and low computational costs. ADFs introduce weights representing the degree of prediction accuracy for each piece of training data and randomly select attribute variables for each node. This method can effectively construct an ensemble model that can predict training data accurately while allowing each decision tree to retain different features. However, outliers can cause overfitting, and since candidates of branch conditions vary for nodes in ADFs, there is a possibility that prediction accuracy will deteriorate because the fitness of training data is highly restrained. In order to improve prediction accuracy, we focus on the prediction results for new data. That is to say, we introduce bootstrap sampling so that the algorithm can generate out-of-bag (OOB) datasets for each tree in the training phase. Additionally, we construct an effective ensemble of decision trees to improve generalization ability by considering the prediction accuracy for OOB data. To verify the effectiveness of the proposed method, we conduct simulation experiments using the UCI machine learning repository. This method provides robust and accurate predictions for datasets with many attribute variables.

    DOI

    Scopus

  • A Latent Class Model to Analyze the Relationship Between Companies' Appeal Points and Students' Reasons for Application

    Teppei Sakamoto, Haruka Yamashita, Tairiku Ogihara, Masayuki Goto

    IPSJ Journal   58 ( 9 ) 1535 - 1548  2017.09  [Refereed]

    Authorship:Last author

    CiNii

  • Multi-Valused Classification of Text Data Based on an ECOC Approach Using a Ternary Orthogonal Table

    Leona Suzuki, Kenta Mikawa, Masayuki Goto

    Industrial Engineering & Management Systems   16 ( 2 ) 155 - 164  2017.07  [Refereed]

    Authorship:Last author

     View Summary

    Because of the advancements in information technology, a large number of document data has been accumulated on various databases and automatic multi-valued classification becomes highly relevant. This paper focuses on a multivalued classification technique that is based on Error-Correcting Output Codes (ECOC) and which combines several binary classifiers. When predicting the category of a new document data, the outputs of the binary classifiers are combined to produce a predicted value. It is a known problem that if two category sets have an imbalanced amount of training data, the prediction accuracy of a binary classifier is low. To solve this problem, a previous study proposed to employ the Reed-Muller (RM) codes in the context an ECOC approach for resolving the imbalance in the cardinality of the training data sets. However, RM codes can equalize the amount of between training data of two category sets only for a specific number of categories. We want to provide a method that can be employed for a multi-valued classification with an arbitrary number of categories. In this paper, we propose a new configuration method combining binary classifiers with categories, which are not used for classification. This method allows us to reduce the amount of training data for each binary classifier while improving the balance of the training data between two category sets for each binary classifier. As a result, the computational complexity can be decreased. We verify the effectiveness of our proposed method by conducting a document classification experiment.

    DOI

    Scopus

    2
    Citation
    (Scopus)
  • A Proposal for Classification of Document Data with Unobserved Categories Considering Latent Topics

    Yusei Yamamoto, Kenta Mikawa, Masayuki Goto

    Industrial Engineering & Management Systems   16 ( 2 ) 165 - 174  2017.07  [Refereed]

    Authorship:Last author

     View Summary

    With rapid development on information society, automatic document classification by machine learning has become even more important. In document classification, it is assumed that a new input data can be classified into any of the categories observed in the training data. Therefore, if a new input data belongs to an unobserved category which does not exist in the training data, then such data cannot be classified exactly. To solve the above problem, Arakawa et al. proposed the method which models the generative probabilities of documents with a mixture of Polya distributions and estimates the optimum category within all observed and unobserved categories where it is assumed that documents in each category are generated from each single Polya distribution. However, the statistical characteristics of document categories are generally more complicated and there are various underlying latent topics in a category. Because a single Polya distribution models each category in the conventional approach, this method cannot represent the variation of word frequency depending on plural unobserved latent topics. This paper proposes a new model which assumes a mixture of Polya distributions for the generative probabilities of documents in a category to represent plural latent topics. To verify the effectiveness of the proposed method, we conduct the simulation experiments of document classification by using a set of English newspaper articles.

    DOI

    Scopus

    4
    Citation
    (Scopus)
  • An efficient learning method using a distributed support vector machine based on controlling data transger

    Kiichiro Yukawa, Kenta Mikawa, Masayuki Goto

    Journal of Japan Industrial Management Association   68 ( 2 ) 86 - 98  2017.07  [Refereed]

    Authorship:Last author

     View Summary

    <p>The developments in information technology have highlighted the importance of analyzing big data stored in various databases. With this as a background, the importance of distributed data mining (DDM), which is the technique of implementing data mining while databases are not transmitting raw data to each other, has been advocated. As one of the methods, Forrero et al. proposed the method of optimal learning with a support vector machine (SVM) that uses the alternating direction method of multipliers (ADMM) in the context of DDM. The apparatus is called a consensus-based distributed support vector machine (D-SVM). This method can learn the optimal hyperplane with a relatively small number of iterations and minimal communication cost for an arbitrary network structure without sharing data. However, when the statistical characteristics of data stored in each database are quite different, this method requires many iterations until convergence. Needless to say, it is better that the number of iterations and total communication cost for the learning classifier are minimized. In this study, we propose a new and effective learning method that reduces the number of iterations considering the network structure, provided that all of the nodes are connected to each other. To verify the effectiveness of the proposed method, a simulation experiment using the UCI machine learning repository and artificial data is conducted.</p>

    DOI CiNii

  • Proposal of a purchase behavior analysis model on EC site considering questionnaire data

    Ryotaro Shimizu, Teppei Sakamoto, Haruka Yamashita, Masayuki Goto

    経営システム   27 ( 2 ) 70 - 76  2017.07  [Invited]

    Authorship:Last author

    CiNii

  • A Proposal of Aspect Model Expressing Both Browsing and Purchasing Behaviors for Customer Purchase Prediction

    Naohiro Fujiwara, Kenta Mikawa, Masayuki Goto

    経営情報学会誌   26 ( 1 ) 1 - 16  2017.06  [Refereed]

    Authorship:Last author

    CiNii

  • A Statistical Analysis Model of Students' Success on Job Hunting by Stratification Tree and Mixed Weibull Distribution

    Mao Hayakawa, Kenta Mikawa, Tairiku Ogihara, Masayuki Goto

    情報処理学会論文誌   58 ( 5 ) 1189 - 1206  2017.05  [Refereed]

    Authorship:Last author

    CiNii

  • The Analysis based on Principal Matrix Decomposition for 3-mode Binary Data

    Haruka Yamashita, Masayuki Goto

    Asian Journal on Management Science and Applications   3 ( 1 ) 24 - 37  2017.04  [Refereed]

    Authorship:Last author

    DOI

  • Data Pair Selection for Accurate Classification Based on Information-theoretic Metric Learning

    Takashi Maga, Kenta Mikawa, Masayuki Goto

    Asian Journal on Management Science and Applications   3 ( 1 ) 61 - 74  2017.04  [Refereed]

    Authorship:Last author

    DOI

  • Distance metric learning based on different ℓ<inf>1</inf> regularized metric matrices in each category

    Kenta Mikawa, Manabu Kobayashi, Masayuki Goto, Shigeichi Hirasawa

    Proceedings of 2016 International Symposium on Information Theory and Its Applications, ISITA 2016     285 - 289  2017.02

     View Summary

    The distance metric learning is the method to learn the distance metric from training data considering its statistical characteristics under the arbitrary constraints. To obtain the desirable distance metric, the optimization problem is solved. Most of the distance metric learning methods aim to gain the global optimal metric matrix. However there is a possibility that the global metric matrix cannot express the statistical characteristics of each category in detail. In addition, if the dimension of input data increase, the computational cost of calculating distance between data increases either. To avoid this problem, we adopt the way to use the l1 regularization to gain sparse metric matrix. By combining those, we focus on the way to deriving the plural metric matrices with a sparse structure in this study. To verify the effective ness of our proposed method, we conduct simulation experiments by using UCI machine learning repository.

  • A Bayes prediction algorithm for model class composed of several subclasses

    Masayuki Goto, Manabu Kobayashi, Kenta Mikawa, Shigeichi Hirasawa

    Proceedings of 2016 International Symposium on Information Theory and Its Applications, ISITA 2016     121 - 125  2017.02

     View Summary

    The Bayes coding algorithm for the tree model class is an effective method calculating the prediction probability of appearing symbol at the next time point from the past data under the Bayes criterion. The Bayes optimal prediction is given by the mixture of all models in a given model class, and the Bayes coding algorithm gives an efficient way to calculate a coding probability. This algorithm is applicable to a general prediction problem with Time-series data. Although the Bayes coding algorithm assumes a class of Markov sources, other model classes can be useful for a real prediction problem in practice. For example, the data at the next time point may not always depend on the strict sequence of the past data. It can be possible to construct an efficient Bayes prediction algorithm for a model class on which the probability of the next symbol is conditioned by the cumulative number in a past data sequence. However, there is usually no way to previously know which model class is the best for the observed data sequence. This paper considers the method to mix the prediction probabilities given by the mixtures on different model subclass. If each calculation of the mixtures on subclasses is efficient, the proposed method is also sufficiently efficient. Based on the asymptotic analysis, we evaluate the prediction performance of the proposed method.

  • EC サイトにおける購買履歴データとアンケートデータを融合した顧客の購買行動分析モデルの提案

    清水 良太郎, 坂元 哲平, 山下 遥, 後藤 正幸

    日本計算機統計学会シンポジウム論文集   31   7 - 10  2017

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  • グルメサービスにおける投稿データと獲得リアクション数の関係分析のための潜在クラスモデル

    坂元 哲平, 山下 遥, 後藤 正幸, 岩永 二郎

    日本計算機統計学会シンポジウム論文集   31   73 - 76  2017

    DOI CiNii

  • Next Generation of Education Model based on Learning Analytics

    Masayuki Goto, Makoto Nakazawa, Katsuyuki Umezawa, Yasuhiro Fujiwara, Yasutoshi Ida, Sotetsu Iwamura

    経営システム   26 ( 3 ) 172 - 179  2016.10  [Invited]

    Authorship:Lead author, Corresponding author

    CiNii

  • Language-independent word acquisition method using a state-transition model

    Bin Xu, Naohide Yamagishi, Makoto Suzuki, Masayuki Goto

    Industrial Engineering and Management Systems   15 ( 3 ) 224 - 230  2016.09  [Refereed]

    Authorship:Last author

     View Summary

    The use of new words, numerous spoken languages, and abbreviations on the Internet is extensive. As such, automatically acquiring words for the purpose of analyzing Internet content is very difficult. In a previous study, we proposed a method for Japanese word segmentation using character N-grams. The previously proposed method is based on a simple state-transition model that is established under the assumption that the input document is described based on four states (denoted as A, B, C, and D) specified beforehand: state A represents words (nouns, verbs, etc.)
    state B represents statement separators (punctuation marks, conjunctions, etc.)
    state C represents postpositions (namely, words that follow nouns)
    and state D represents prepositions (namely, words that precede nouns). According to this state-transition model, based on the states applied to each pseudo-word, we search the document from beginning to end for an accessible pattern. In other words, the process of this transition detects some words during the search. In the present paper, we perform experiments based on the proposed word acquisition algorithm using Japanese and Chinese newspaper articles. These articles were obtained from Japan's Kyoto University and the Chinese People's Daily. The proposed method does not depend on the language structure. If text documents are expressed in Unicode the proposed method can, using the same algorithm, obtain words in Japanese and Chinese, which do not contain spaces between words. Hence, we demonstrate that the proposed method is language independent. &amp
    copy
    2016 KIIE.

    DOI

    Scopus

  • A Study of Distance Metric Learning by Considering the Distances between Category Centroids

    Kenta Mikawa, Manabu Kobayashi, Masayuki Goto, Shigeichi Hirasawa

    Proceedings - 2015 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2015   ( IEEE SMC2015 ) 1645 - 1650  2016.01  [Refereed]

     View Summary

    In this paper, we focus on pattern recognition based on the vector space model. As one of the methods, distance metric learning is known for the learning metric matrix under the arbitrary constraint. Generally, it uses iterative optimization procedure in order to gain suitable distance structure by considering the statistical characteristics of training data. Most of the distance metric learning methods estimate suitable metric matrix from all pairs of training data. However, the computational cost is considerable if the number of training data increases in this setting. To avoid this problem, we propose the way of learning distance metric by using the each category centroid. To verify the effectiveness of proposed method, we conduct the simulation experiment by using benchmark data.

    DOI

    Scopus

  • A study on distance metric learning by using different metric matrices in each category

    Kenta Mikawa, Masayuki Goto

    Journal of Japan Industrial Management Association   66 ( 4 ) 335 - 347  2016.01  [Refereed]

    Authorship:Last author

     View Summary

    Distance metric learning is the method of learning the relevant distance metric from a training dataset by considering statistical characteristics. In order to gain a desirable distance metric, the optimization problem under an arbitrary constraint is solved. However, the representative algorithms of distance metric learning need to perform eigendecomposition at each iteration. Therefore, if the dimensions of the input data become large, the computational cost will increase drastically and it is difficult to calculate the optimal solution in a realistic amount of time. In addition, those distance metric learning methods are formulated by assuming a global (unique) metric matrix for the total vector space. Therefore, the global metric matrix cannot take into account the difference in statistical characteristics between each category. To improve those problems, the authors introduce different metric matrices for each category and propose a way to estimate the plural matrices using category information that applies the method of Mochihashi et al. The estimated metric matrices reflect the statistical characteristics of each category. The formulation of classifications by template matching and the k-NN method making effective use of the metric matrices is proposed. To verify the effectiveness of the proposed method, a simulation experiment is conducted using the benchmark data of high-dimensional and low-dimensional input data.

    DOI CiNii

  • A New Latent Class Model for Analysis of Purchasing and Browsing Histories on EC Sites

    Masayuki Goto, Kenta Mikawa, Shigeichi Hirasawa, Manabu Kobayashi, Tota Suko, Shunsuke Horii

    Industrial Engineering and Management Systems   14 ( 4 ) 335 - 346  2015.12  [Refereed]

    Authorship:Lead author

     View Summary

    The electronic commerce site (EC site) has become an important marketing channel where consumers can purchase many kinds of products
    their access logs, including purchase records and browsing histories, are saved in the EC sites' databases. These log data can be utilized for the purpose of web marketing. The customers who purchase many product items are good customers, whereas the other customers, who do not purchase many items, must not be good customers even if they browse many items. If the attributes of good customers and those of other customers are clarified, such information is valuable as input for making a new marketing strategy. Regarding the product items, the characteristics of good items that are bought by many users are valuable information. It is necessary to construct a method to efficiently analyze such characteristics. This paper proposes a new latent class model to analyze both purchasing and browsing histories to make latent item and user clusters. By applying the proposal, an example of data analysis on an EC site is demonstrated. Through the clusters obtained by the proposed latent class model and the classification rule by the decision tree model, new findings are extracted from the data of purchasing and browsing histories.

    DOI

    Scopus

    14
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  • A study of learning a sparse metric matrix using l<inf>1</inf> regularization based on supervised learning

    Kenta Mikawa, Manabu Kobayashi, Masayuki Goto

    Journal of Japan Industrial Management Association   66 ( 3 ) 230 - 239  2015.10  [Refereed]

    Authorship:Last author

     View Summary

    In this paper, we focus on classification problems based on the vector space model. As one of the methods, distance metric learning which estimates an appropriate metric matrix for classification by using the iterative optimization procedure is known as an effective method. However, the distance metric learning for high dimensional data tends to cause the problems of overfitting to a training dataset and longer computational time. In addition, the number of parameters that need to be estimated is in proportion to the square of the input data dimension. Therefore, if the dimension of input data becomes high, the number of training data to acquire a metric matrix with enough accuracy becomes enormous. Especially, these problems are caused when analyzing the document data and purchase history data stored in the EC site with high dimensional and sparse structure. To avoid these problems, we propose the method of l1 regularized distance metric learning by introducing the alternating direction method of multiplier (ADMM) algorithm. The effectiveness of our proposed method is clarified by classification experiments using a newspaper article that has a highly dimensional and sparse structure and the UCI machine learning repository, which has a low and dense structure.

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  • An Analysis of Customers’ Characteristics of Good and Estranged Customers Based on Membership Stage

    Tetsuya Sakai, Kenta Mikawa, Masayuki Goto

    経営システム   25 ( 3 ) 182 - 187  2015.10  [Invited]

    Authorship:Last author

    CiNii

  • Regularized distance metric learning for document classification and its application

    Kenta Mikawa, Masayuki Goto

    Journal of Japan Industrial Management Association   66 ( 2E ) 190 - 203  2015.07  [Refereed]

    Authorship:Last author

     View Summary

    Due to the development of information technologies, there is a huge amount of text data posted on the Internet. In this study, we focus on distance metric learning, which is one of the models of machine learning. Distance metric learning is a method of estimating the metric matrix of Mahalanobis squared distance from training data under an appropriate constraint. Mochihashi et al. proposed a method which can derive the optimal metric matrix analytically. However, the vector space for document data is normally very high dimensionally and sparse. Therefore, when this method is applied to document data directly, over-fitting may occur because the number of estimated parameters is in proportion to the square of the input data dimensions. To avoid the problem of over-fitting, a regularization term is introduced in this study. The purpose of this study is to formulate the regularized estimation of the metric matrix in which the optimal metric matrix can be derived analytically. To verify the effectiveness of the proposed method, document classification using a Japanese newspaper article is conducted.

    DOI

  • Accurate parameter estimation based on latent class model estimated by combining both evaluation and purchase histories

    Takahiro Oi, Kenta Mikawa, Masayuki Goto

    Journal of Japan Industrial Management Association   65 ( 4 ) 286 - 293  2015.01  [Refereed]

    Authorship:Last author

     View Summary

    Recently, the recommender system which automatically recommends product items for customers has become more important as an efficient web marketing tool. In many electronic commerce (EC) sites, the data of customers purchases and evaluation histories are stored in a database. By using them, the system predicts users preferences, and automatically recommends items that seem to be preferred, but have not been purchased yet. In this study, we focus on the recommender system based on collaborative filtering (CF) with a latent class model. CF recommends items by using purchase or evaluation history data. Considering real purchase activity on EC sites, most of the consumers who bought items on an EC site dont post their evaluation on the site. That means more purchase history data is stored more in the database than evaluation history data. However, most studies of CF used only evaluation data to learn the model. In this case, the purchase data is not used to construct a model even though its data size is much larger than that of evaluation history. It is more desirable to learn a model by using not only evaluation history, but also purchase data to improve the CF accuracy. The purpose of this study is to construct an effective CF model to improve the CF accuracy by formulating the estimation using both evaluation history data and abundant purchase history data which has not been used in previous CF studies. Specifically, we use the aspect model, which is one of latent class models of CF. We propose a way to estimate its parameters using both evaluation history and purchase data. To verify the effectiveness of this study, a simulation experiment was conducted using a bench mark data of recommender system. We show that the prediction accuracy of the recommendation is improved.

    DOI CiNii

  • Foreword: Special section on information theory and its applications

    Toshiyasu Matsushima, Manabu Kobayashi, Shiro Ikeda, Shogo Usami, Kenta Kasai, Shigeaki Kuzuoka, Hiroki Koga, Tetsuya Kojima, Masayuki Goto, Tatsumi Konishi, Hidetoshi Saito, Ryuichi Sakai, Mikihiko Nishiara, Ryo Nomura, Mitsuru Hamada, Masaya Fujisawa, Tetsunao Matsuta, Ryutaroh Matsumoto, Kazuhiko Minematsu, Kazushi Mimura, Hideki Yagi

    IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences   E97A ( 12 ) 2287  2014.12

  • A Survey for Sustainable Development of Tourism in Nepal

    Yo Nishihara, Naohiro Fujiwara, Masayuki Goto, Brenda Bushell

    Interdisciplinary Environmental Review   15 ( 4 ) 239 - 251  2014.12  [Refereed]

    DOI

  • A Modified Aspect Model for Simulation Analysis

    Masayuki Goto, Kazushi Minetoma, Kenta Mikawa, Manabu Kobayashi, Shigeichi Hirasawa

    IEEE International Conference on Systems, Man, and Cybernetics   2014-January ( IEEE SMC2014 ) 1306 - 1311  2014.10  [Refereed]

    Authorship:Lead author, Corresponding author

     View Summary

    This paper proposes a new latent class model to represent user segments in a marketing model of electric commerce sites. The aspect model proposed by T. Hofmann is well known and is also called the probabilistic latent semantic indexing (PLSI) model. Although the aspect model is one of effective models for information retrieval, it is difficult to interpret the meaning of the probability of latent class in terms of marketing models. It is desirable that the probability of latent class means the size of customer segment for the purpose of marketing research. Through this formulation, the simulation analysis to dissect the several situations become possible by using the estimated model. The impact of the strategy that we contact to the specific customer segment and make effort to increase the number of customers belonging to this segment can be predicted by using the model demonstrating the size of customer segment. This paper proposes a new model whose probability parameter of latent variable means the rate of users with the same preference in market. By applying the proposed model to the data of an internet portal site for job hunting, the effectiveness of our proposal is verified.

    DOI

    Scopus

    2
    Citation
    (Scopus)
  • Robustness of syndrome analysis method in highly structured fault-diagnosis systems

    Manabu Kobayashi, Masayuki Goto, Toshiyasu Matsushima, Shigeichi Hirasawa

    Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics   2014-January ( January ) 2757 - 2763  2014.10  [Refereed]

     View Summary

    F. P. Preparata et al. proposed a fault diagnosis model (PMC model) to find all fault units in the multicomputer system by using outcomes that each unit tests some other units. T. Kohda proposed a highly structured(HS) system and the syndrome analysis method(SAM) to diagnose from local testing results. In this paper, we introduce the maximum a posteriori probability algorithm(MAPDA) for the HS system in the probabilistic fault model. Analyzing the MAPDA, we show that the SAM is closer to the MAPDA as the fault probability becomes smaller. Finally, we show the robustness of the SAM in the HS system.

    DOI

    Scopus

    1
    Citation
    (Scopus)
  • A proposal of l<inf>1</inf> regularized distance metric learning for high dimensional sparse vector space

    Kenta Mikawa, Manabu Kobayashi, Masayuki Goto, Shigeichi Hirasawa

    Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics   2014-January ( January ) 1985 - 1990  2014.10  [Refereed]

     View Summary

    In this paper, we focus on pattern recognition based on the vector space model with the high dimensional and sparse data. One of the pattern recognition methods is metric learning which learns a metric matrix by using the iterative optimization procedure. However most of the metric learning methods tend to cause overfitting and increasing computational time for high dimensional and sparse settings. To avoid these problems, we propose the method of l1 regularized metric learning by using the algorithm of alternating direction method of multiplier (ADMM) in the supervised setting. The effectiveness of our proposed method is clarified by classification experiments by using the Japanese newspaper article and UCI machine learning repository. And we show proposed method is the special case of the statistical sparse covariance selection.

    DOI

    Scopus

    1
    Citation
    (Scopus)
  • A Practice and Evaluation of Environmental Education Program for University Students through the Nepal Field Program

    Masayuki Goto, Manita Shrestha, Syuji Yagyu, Brenda Bushell

    環境教育   24 ( 1 ) 160 - 169  2014.07  [Refereed]

    Authorship:Lead author, Corresponding author

    DOI CiNii

  • Proposal of a semiautomatic classification method for systematization of large-scale text data based on machine learning

    Ryo Shimomura, Kenta Mikawa, Masayuki Goto

    Journal of Japan Industrial Management Association   65 ( 2 ) 51 - 60  2014.07  [Refereed]

    Authorship:Last author

     View Summary

    These days, many companies store enormous amounts of text data used in their operations in digital format because computers are now used in all processes in every department. However, the valuable information in this enormous amount of text data often cannot be used effectively. Normally, such data contains a lot of useful information for company workers and it is important to use the data effectively for the development of companies. However, the volume of the text data is sometimes too enormous to use the data directly. Even if analysts spend a lot of time in order to extract useful information from the text data, it may be impossible to analyze such huge amounts of text data. Generally, clustering or grouping by similarity and naming each group to provide category information are effective ways to grasp the tendency of the whole data and systematize many ideas. However, analysis by hand can only be carried out for small volumes of data in which analysts can see all data items or ideas. Therefore, it is difficult to apply clustering by hand to the large-scale text data which is stored in companies. If clustering and naming by analysts can be applied to enormous amounts of text data, it will be useful for extracting valuable information. In this study, we propose a new method based on the combination of clustering by hand and text classification in order to effectively analyze large-scale digital data which is stored in a company. The first step of the proposed method is to provide category information by hand for the sample data selected randomly from all the text data. The next step is to estimate classifiers through learning of this sample data, and to classify the rest of the data using the classifiers automatically. Using the proposed method, enormous amounts of text data can be systemized provided that only a small sample set is analyzed by hand. To verify the effectiveness of the proposed method, it is applied to the large-scale text data which was stored in a company as a case study.

    DOI CiNii

  • Privacy-preserving distributed calculation methods of a least-squares estimator for linear regression models

    Tota Suko, Shunsuke Horii, Manabu Kobayashi, Masayuki Goto, Toshiyasu Matsushima, Shigeichi Hirasawa

    Journal of Japan Industrial Management Association   65 ( 2 ) 78 - 88  2014.07  [Refereed]

     View Summary

    In this paper, we study a privacy preserving linear regression analysis. We propose a new protocol of a distributed calculation method that calculates a least squares estimator, in the case that two parties have different types of explanatory variables. We show the security of privacy in the proposed protocol. Because the protocol have iterative calculations, we evaluate the number of iterations via numerical experiments. Finally, we show an extended protocol that is a distributed calculation method for k parties.

    DOI CiNii

  • A design of recommendation based on flexible mixture model considering purchasing interest and post-purchase satisfaction

    Takeshi Suzuki, Gendo Kumoi, Kenta Mikawa, Masayuki Goto

    Journal of Japan Industrial Management Association   64 ( 4 ) 570 - 578  2014.01  [Refereed]

    Authorship:Last author

     View Summary

    The recommender system is an effective Web marketing tool that havve been used especially on electric commerce sites in recent years. The recommender system provides each user with a list of new recommended items that are predicted to be preferred by the user. Collaborative filtering is one of the most representative and powerful methods to predict user preference in the recommender system. Collaborative filtering measures the similarity of preference between users and uses it to decide items to be recommended. Based on previous researche on this method, user preference is considered to have two aspects: Purchasing interest for items and post-purchase satisfaction with items. However, the conventional methods do not consider the two different preferences at the same time. This paper suggests taking these two preferences into account and proposes a new method that allows users to choose the balance between them. The proposed method is evaluated through simulation experiments with MovieLens data. It demonstrates the effectiveness of our proposal in precision and average rating compared with a previous method.

    DOI CiNii

  • Similarity Matrix Based on Random Forest for Clustering of Incomplete Data

    Yuki Sanada, Takahiro Ooi, Takashi Ishida, Masayuki Goto

    電子情報通信学会論文誌D   J97-D ( 1 ) 239 - 243  2014.01  [Refereed]

    Authorship:Last author

    CiNii

  • A Bayes optimal predicting method of Bayesian network with visualization of causal relationship

    Yusuke Takeyama, Takashi Ishida, Masayuki Goto

    Journal of Japan Industrial Management Association   64 ( 3 ) 399 - 408  2013.10  [Refereed]

    Authorship:Last author

     View Summary

    A Bayesian network is one of the useful models for pattern recognition problems and it has the features of both stochastic prediction and causal models. A Bayesian network expresses the causal relationship between variables with directed graphs. Usually the structure of a Bayesian network is statistically estimated using a set of training data and the model selection has been applied in conventional methods when Bayesian network structures were estimated. However, it is not necessary to choose one model for the purpose of prediction. From the viewpoint of Bayesian statistics, it is well known that prediction using the mixture model on model class is Bayes optimal. In general, the mixture model that is given by a weighted sum of all models with the posterior probability on the model class is the Bayes optimal prediction. In this paper, we propose an new Bayes optimal prediction on a Bayesian network model class using the mixture model. A mixture model sometimes becomes a complex expression due to the weighted sum of all models on a model class, and it results in loss of the usefulness as a causal model. Since the easiness of interpretation is one of the merits of a Bayesian network, using a mixed model only for improvement in predictive accuracy may lead to losing the merit of a Bayesian network. Therefore, we propose a new method that is configured with the mixture model utilizing the characteristics of the Bayesian network by organizing model classes properly. Furthermore, we propose a method to quantitatively assess the strength of the causal relationship between the nodes on the mixed Bayesian network model. In addition, the effectiveness of the proposed method is clarified via a numerical experiment on an application to a prediction problem of buying and selling of shares stock market.

    DOI

  • Text classification using similarity of tree sources estimated from Bayes coding algorithm

    Hiroki Iwama, Takashi Ishida, Masayuki Goto

    Journal of Japan Industrial Management Association   64 ( 3 ) 438 - 446  2013.10  [Refereed]

    Authorship:Last author

     View Summary

    In this paper, we propose a method of text classification using a Bayes coding algorithm, one of the efficient data compression methods. The Bayes coding algorithm gives the Bayes optimal data compression over the tree source model class. When data is compressed by the Bayes coding algorithm, the probability structure of information sources is implicitly estimated from the compressed data. Therefore, we can expect that the implicit estimation of data compression can be utilized for other purposes, especially for the document classification problem. As for the document classification using data compression methods, ZIP format and context tree weighting methods have been proposed. However, these methods do not have Bayes optimal compression and use the compression ratio as a similarity measure between documents for classification. In the Bayes coding algorithm, a weighted mixture tree given by the compression phase can be used for estimated probability structure. Tree source is a class of Markov sources and it is possible to measure the divergence between the tree sources with the same structure. However, the Bayes coding algorithm outputs different tree structures based on the data sequence to be compressed. Since the tree structures derived from documents are different from each other, it is difficult to measure the divergence between them just as it is. This paper proposes a new method to change the structures of weighted mixture trees into the same tree structure to be able to measure the divergence. Using the divergence between trees estimated by documents, the documents can be classified. Moreover, the effectiveness of the proposed method is clarified via a simulation experiment for the document classification with natural data.

    DOI CiNii

  • Multi-valued Document Classification Based on Generalized Bradley-Terry Classifiers Utilizing Accuracy Information

    Tairiku Ogihara, Kenta Mikawa, Masayuki Goto, Gou Hosoya

    China-USA Business Review   12 ( 9 ) 911 - 917  2013.09  [Refereed]

  • A Study on Evaluation of Model-based Collaborative Filtering Using Agent-based Simulation

    Yusuke Izawa, Kenta Mikawa, Masayuki Goto

    経営情報学会誌   22 ( 2 ) 95 - 106  2013.09  [Refereed]

    Authorship:Last author

    CiNii

  • A Study on Document Classification Method with Containig Unknown Categories

    Takanori Arakawa, Kenta Mikawa, Masayuki Goto

    電子情報通信学会論文誌D   J96-D ( 8 ) 1956 - 1959  2013.08  [Refereed]

    Authorship:Last author

    CiNii

  • Rate-compatible Punctured LDPC Codes with Two Subgraphs

    Gou Hosoya, Keishi Osada, Masayuki Goto

    Far East Journal of Electronics and Communications   10 ( 2 ) 83 - 104  2013.06  [Refereed]

    Authorship:Last author

     View Summary

    A new ensemble of rate-compatible punctured low-density paritycheck (LDPC) codes is presented. By demonstrating the code performance analytically using the Gaussian approximation, we derive a good puncturing distribution of the proposed irregular LDPC codes. From this distribution, we show that the iterative thresholds of the proposed codes for each puncturing rate are larger than those of the standard LDPC codes. We also confirm performance of the proposed LDPC code by simulations. © 2013 Pushpa Publishing House.

  • Information Analysis and Processing in Industrial Engineering

    Masayuki Goto

    経営システム   23 ( 1 ) 48 - 57  2013.04  [Invited]

    Authorship:Lead author, Last author, Corresponding author

  • A Predictive Model of Number of Customers for Restaurant Chain Based on Bayesian Model Averaging

    Masayuki Goto, Yoichi Komiya, Takashi Ishida, Tadayuki Masui

    Innovation and Supply Chain Management   6 ( 3 ) 91 - 98  2012.09  [Refereed]

    Authorship:Lead author

  • An Optimal Weighting Method in Supervised Learning of Linguistic Model for Text Classification

    Kenta Mikawa, Takashi Ishida, Masayuki Goto

    Industrial Engineering & Management Systems   Vol.11 ( No.1 ) 87 - 93  2012.03  [Refereed]

    Authorship:Last author

    DOI

  • Interactive Genetic Algorithm to Estimate Weight Parameters of Evaluation Function

    Eitaro Ishikawa, Takashi Isida, Masayuki Goto

    電子情報通信学会論文誌D   Vol.J94-D ( No.11 ) 1888 - 1898  2011.11  [Refereed]

    CiNii

  • A proposal of extended cosine measure for distance metric learning in text classification

    Kenta Mikawa, Takashi Ishida, Masayuki Goto

    Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics   ( IEEE SMC2011 ) 1741 - 1746  2011.10  [Refereed]

    Authorship:Last author

     View Summary

    This paper discusses a new similarity measure between documents on a vector space model from the view point of distance metric learning. The documents are represented by points in the vector space by using the information of frequencies of words appearing in each document. The similarity measure between two different documents is useful to recognize the relationship and can be applied to classification or clustering of the data. Usually, the cosine similarity and the Euclid distance have been used in order to measure the similarity between points in the Euclidean space. However, these measures do not take the correlation among words which appear in documents into consideration on an application of the vector space model to document analysis. Generally speaking, many words which appear in documents have correlation to one another depending on the sentence structures, topics and subjects. Therefore, it is effective to build a suitable metric measure taking the correlation of words into consideration on the vector space in order to improve the performance of document classification and clustering. This paper presents a new effective method to acquire a distance measure on the document vector space based on an extended cosine measure. In addition, the way of distance metric learning is proposed to acquire the proper metric from the view point of supervised learning. The effectiveness of our proposal is clarified by simulation experiments for the text classification problems of the customer review which is posted on the web site and the newspaper article. © 2011 IEEE.

    DOI

    Scopus

    18
    Citation
    (Scopus)
  • Report: Nepal-Japan Collaborative Field Study in Nepal

    Itaru Sugano, Masashi Kobatake, Mai Sasaki, Yushiro Suzuki, Yuko Ushiki, Akira Okada, Hom B. Rijal, Manita Shrestha, Masayuki Goto, Brenda Bushell

    Journal of Development Studies, National College, Samriddhi   2 ( 1 ) 48 - 51  2011.04  [Refereed]

  • Leadership for sustainable society: A transformative learning approach

    Brenda Bushell, Masayuki Goto

    Procedia - Social and Behavioral Sciences   29   1244 - 1250  2011

     View Summary

    Over the past several decades the international community has identified education and capacity building as critical components in helping to shift societies toward sustainable development in a global context. However, despite calls for fostering curriculum design around education for sustainability, colleges and universities have been slow to formulate ideas and approaches to teaching. This paper introduces a collaborative educational partnership which attempts to weave in the various components connected to education for sustainability. Transformative learning theory is used together with other frameworks to guide the program and evaluate the learning outcomes of students. © 2011 Published by Elsevier Ltd.

    DOI

    Scopus

    4
    Citation
    (Scopus)
  • A Pilot Study for the Construction of Sustainable Community Indicators in Rural Nepal

    Brenda Bushell, Nozomi Imai, Mari Naitoh, Masayuki Goto

    Interdisciplinary Environmental Review   11 ( 4 ) 303 - 321  2011  [Refereed]

    Authorship:Last author

    DOI

  • Educating for Sustainability: A Pilot Study in an Elementary School in Rural Nepal

    Brenda Bushell, Nozomi Imai, Mari Naitoh, Masayuki Goto

    Interdisciplinary Environmental Review   12 ( 1 ) 12 - 23  2011  [Refereed]

    Authorship:Last author

    DOI

  • A Study on Improvement of Delivery Efficiency for Home Delivery Service

    Miho Suzuki, Tomoe Tomita, Masayuki Goto, Tadayuki Masui

    Journal of the Japan Society for Management Information   19 ( 3 ) 235 - 258  2010.12  [Refereed]

    CiNii

  • On a New Model for Automatic Text Categorization Based on Vector Space Model

    Makoto Suzuki, Naohide Yamagishi, Takashi Ishida, Masayuki Goto, Shigeichi Hirasawa

    IEEE International Conference on Systems, Man, and Cybernetics   ( IEEE SMC2010 ) 3152 - 3159  2010.10  [Refereed]

    DOI

  • A theoretical analysis of document classification based on a high-dimensional vector space model - Asymptotic analysis of classification performance and distance measures

    Masayuki Goto, Takashi Ishida, Makoto Suzuki, Shigeichi Hirasawa

    Journal of Japan Industrial Management Association   61 ( 3 ) 97 - 106  2010.08  [Refereed]

    Authorship:Lead author, Corresponding author

     View Summary

    Problems associated with document classification, an important application of text mining of text data, are focused on in this paper. There have been many models and algorithms proposed for text classification; one of these is a technique using a vector space model. In these methods, a digital document is represented as a point in the vector space which is constructed by morphological analysis and counting the frequency of each word in the document. In the vector space model, the documents can be classified using the distance measure between documents. However, there are specific characteristics in the vector space model for document classification. Firstly, it is not easy to automatically remove unnecessary words completely. The existence of unnecessary words is one of the characteristics of the text mining problems. Secondly, the dimensions of the word vector space are usually huge in comparison to the number of words appearing in a document. Although the frequencies of words appearing in a document could be small in many cases, many kinds of such words with small frequency can usually be used to classify the documents. In this paper, we evaluate the performance of document classification in the case where unnecessary words are included in the word set. Moreover, the performance of the distance measure between documents in a large dimensional word vector space is analyzed. From the asymptotic results about the distance measure, we can provide an explanation of the fact given in many experiments that classification using the empirical distance between documents calculated via the cosine measure is not particularly bad. It is also suggested that the KL-divergence is not useful for text mining problems.

    CiNii

  • Developing Effective Multimedia Educational Contents: Research and Design

    Brenda Bushell, Keiko Shimizu, Manami Shiihashi, Yoshiteru Takinoiri, Masayuki Goto, Akira Okada, Kuniko Yoshida

    東京都市大学 環境情報学部 情報メディアセンタージャーナル   11 ( 11 ) 79 - 91  2010.04

    CiNii

  • Development of Multimedia Environmental Educational Content by Cyber-Nepal Project

    ブレンダ ブッシェル, 後藤 正幸, 岡田 啓

    東京都市大学 環境情報学部 情報メディアセンタージャーナル   10 ( 10 ) 28 - 33  2009.04

    CiNii

  • A Study on Brand Image Measurement Method Using Web Search Engine

    中村 徹, 富田 大介, 後藤 正幸

    東京都市大学 環境情報学部 情報メディアセンタージャーナル   10 ( 10 ) 119 - 127  2009.04

    Authorship:Last author

    CiNii

  • A Study on Customer Loyalty Improvement Factor for Professional Baseball

    坂田 和典, 田中 慶二, 後藤 正幸

    東京都市大学 環境情報学部 情報メディアセンタージャーナル   10 ( 10 ) 109 - 118  2009.04

    Authorship:Last author

    CiNii

  • A study of quantification method for important factors based on customer loyalty structure diagram

    Kenta Mikawa, Tadayuki Masui, Masayuki Goto

    Journal of Japan Industrial Management Association   59 ( 5 ) 365 - 375  2008.12  [Refereed]

    Authorship:Last author

     View Summary

    Recently, it is said building customer loyalty is effective to keep the relationship between customers and companies developing. However due to this relationship's complex characteristics, there are few ways to strengthen loyalty. Consequently, it is important for company markets to grasp and calculate the important factors of customer loyalty. In order to analyze complex customer characteristics or their loyalty, one needs to analyze not only data from questionnaires, but also free-format text data. This is because the respondent can answer their opinion freely, so the collection of more information and the finding of new opinions can be expected. Recently, due to the development of information technology, people can obtain or store enormous amounts of such customer data easily. This research analyzes this free-format text data by using morphological analysis, one of the methods of natural language processing. Using this result, we have reconstructed the customer loyalty structure diagram, by using a method of analyzing customer loyalty, which was proposed by previous research. In addition to evaluating the important index of customer loyalty, we propose a method of calculation using the customer loyalty structure diagram. Moreover, this research is aiming for the evaluation of the difference between strongly satisfied and unsatisfied customers. We also investigate the difference of characteristics between product categories. From those results, corporate marketers can know what the key factors of customer loyalty are important and make a strategy to get excellent customers.

    DOI

  • A study on construction support for web sites using an affiliate program based on intellectual structuring and feature analysis

    Norikatsu Saito, Masayuki Goto

    Journal of Japan Industrial Management Association   59 ( 2 ) 145 - 154  2008.06  [Refereed]

    Authorship:Last author

     View Summary

    Recently, the use of affiliate sites is recognized as a way of Internet business without incurring a huge initial cost. However, there is a problem that half of the persons who are managing the affiliate sites can be earning less than 1,000 yen a month. Because this problem obstructs the development of a continuous affiliate business, it should be overcome. If the knowledge of excellent managers who are earning high profits can be used and a highly efficient way is clarified, then it will be helpful to affiliate sites operated by novices. Many interview comments and suggestions by excellent managers can be found in journals. However, an affiliate site novice cannot make use of the knowledge of excellent affiliate site managers unless it is systematic and well known. In this paper, new methods to analyze the knowledge of excellent affiliate managers and features of excellent affiliate sites are proposed. The first proposal is a technique to construct a system structure based on the techniques of excellent affiliate managers, taken from the interviews printed in journals. The system constructed here shows a list of important key points and is a systematical representation of the knowledge of excellent affiliate site managers. The second proposal is a method to clarify the features of an individual affiliate site. To do this, the items to be checked items are listed. The methodology of a useful analysis to construct and manage affiliate sites is established applying these two viewpoints. Additionally, the effectiveness of the technique proposed in this paper is clarified by applying of our proposal to a set of excellent affiliate sites.

    DOI

  • 高齢者のパソコン学習の継続性を考える −第3回高齢者パソコン教室の事例から−

    野田琢海, 中村雅子, 後藤正幸

    武蔵工業大学環境情報学部, 情報メディアセンタージャーナル   9 ( 9 ) 120 - 130  2008.04

    Authorship:Last author

    CiNii

  • 高齢者向けPC教室における学生ボランティアのベネフィット分析に関する研究

    渡部大樹, 後藤正幸, 中村雅子

    武蔵工業大学環境情報学部, 情報メディアセンタージャーナル   9 ( 9 ) 111 - 119  2008.04

    CiNii

  • 宿泊施設の戦略構築を支援するユーザレビュー分析に関する一考察

    田邊 亘, 後藤正幸

    武蔵工業大学環境情報学部, 情報メディアセンタージャーナル   9 ( 9 ) 91 - 101  2008.04

    Authorship:Last author

    CiNii

  • 中古車の価格モデルとユーザベネフィット分析に関する研究

    田中慶二, 富田大介, 後藤正幸

    武蔵工業大学環境情報学部, 情報メディアセンタージャーナル   9 ( 9 ) 102 - 110  2008.04

    Authorship:Last author

    CiNii

  • 物流プロセスにおける商品1点あたり二酸化炭素排出量の算定法

    増井忠幸, 後藤正幸, 吉藤智一

    武蔵工業大学環境情報学部紀要   9   6 - 16  2008.02

    CiNii

  • Statistical Evaluation of Measure and Distance on Document Classification Problems in Text Mining

    Masayuki Goto, Takashi Ishida, Shigeich Hirasawa

    IEEE International Conference on Computer and Information Technology   ( CIT2007 ) 674 - 679  2007.10  [Refereed]

    Authorship:Lead author

    DOI

  • Study of a structure analysis method for customer loyalty based on text data

    Kenta Mikawa, Tsutomu Takahashi, Masayuki Goto

    Journal of Japan Industrial Management Association   58 ( 3 ) 182 - 192  2007.08  [Refereed]

    Authorship:Last author

     View Summary

    Recently, marketing activities are gradually shifting from making new customers to keeping current customers because of the intensifying market competition. Hence, keeping customers is related to improving customer satisfaction and customer loyalty. Ultimately keeping a customer means improving of lifetime value. In addition, it has been found that there are many effects for the company. Therefore, companies aiming to improve their profits need to focus on the components of customer loyalty. However, it is difficult to specify those factors because the structure of customer loyalty is very complex and its composition differs from customer to customer. This research uses a methodology interview and free-ended questionnaire data to map the customer loyalty structure. In order to clarify constitutes customer loyalty, the method of morphological analysis as the way of natural language techniques is introduced. This is used because there is much free-ended text data that companies can accumulate easily as the result of developing information technologies. Using these data, text mining can be applied, which is one of the important ways of obtaining information for CRM(customer relationship management). Through analysis, using the concept of text mining and the introduction of knowledge of the field in the study of customer loyalty, we find the structure of loyalty and consider how to improve these assumptions. Principal component analysis using the structure extracted from the text type comments of customers and vector spaces made by the structure and word frequency in each text gives us various findings about customer loyalty.

    DOI

  • A mixed distribution system of direct and relay delivery methods based on an analogy of milk-run logistics

    Masayuki Goto, Tadayuki Masui, Nobuhiko Tawara

    Journal of Japan Industrial Management Association   58 ( 2 ) 79 - 86  2007.06  [Refereed]

    Authorship:Lead author, Corresponding author

     View Summary

    This paper proposes a new physical distribution model to express a mixed system of direct and relay delivery methods from the manufacturer to the shop. The direct and relay delivery methods should be selected and appropriately applied depending on the situations. However, a system with the intermediate characteristics of these two methods may be effective for some settings in practice and is proposed in this paper. This method is based on an analogy of the milk-run distribution method. Milk-run logistics is a method such that the manufacturer's trucks begin at the factory, move to suppliers, collect parts at each supplier's plant, and return to the factory instead of the usual distribution by suppliers. Though milk-run logistics is an effective method as an inbound logistics system, this paper identifies how an effective distribution system can be constructed for outbound logistics. A basic system model is shown for a distribution model with three layers, including an analysis of the model by Z transform. From the numerical examples, the effectiveness of the proposal is clarified. In the proposed system, the variances of inventories in the relay distribution center and shop can be reduced.

    DOI CiNii

  • 価格プレミアムの評価と要因分析手法に関する一考察

    林翔希, 渡辺智幸, 後藤正幸

    武蔵工業大学環境情報学部, 情報メディアセンタージャーナル   8 ( 8 ) 87 - 94  2007.04

    Authorship:Last author

    CiNii

  • 高齢者向けパソコン教室を通した学習環境のデザイン

    吉村友佑, 中村雅子, 後藤正幸

    武蔵工業大学環境情報学部, 情報メディアセンタージャーナル   8 ( 8 ) 63 - 69  2007.04

    Authorship:Last author

    CiNii

  • 孫との関係に着目した高齢者の主観的幸福感に関する研究

    中村辰哉, 浜翔太郎, 後藤正幸

    武蔵工業大学環境情報学部, 情報メディアセンタージャーナル   8 ( 8 ) 75 - 86  2007.04

    Authorship:Last author

    CiNii

  • Targeting Partnerships Toward a Model of Community Waste Management: A Case Study in Nepal

    Brenda Bushell, Masayuki Goto

    Interdisciplinary Environmental Review   8 ( 2 ) 51 - 51  2006.12  [Refereed]

    Authorship:Last author

    DOI

  • Properties of a Word-valued Source with a Non-prefix-free Word Set

    Takashi Ishida, Masayuki Goto, Toshiyasu Matsushima, Shigeichi Hirasawa

    IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences   E89A ( 12 ) 3710 - 3723  2006.12  [Refereed]

     View Summary

    Recently, a word-valued source has been proposed as a new class of information source models. A word-valued source is regarded as a source with a probability distribution over a word set. Although a word-valued source is a nonstationary source in general, it has been proved that an entropy rate of the source exists and the Asymptotic Equipartition Property (AEP) holds when the word set of the source is prefix-free. However, when the word set is not prefix-free (non-prefix-free), only an upper bound on the entropy density rate for an i.i.d. word-valued source has been derived so far. In this paper, we newly derive a lower bound on the entropy density rate for an i.i.d. word-valued source with a finite non-prefix-free word set. Then some numerical examples are given in order to investigate the behavior of the bounds.

    DOI

    Scopus

    3
    Citation
    (Scopus)
  • 自然言語情報の分析手法と経営学的諸問題への応用

    後藤 正幸

    武蔵工業大学 環境情報学部 紀要   8   94 - 104  2006.10

    Authorship:Lead author, Last author, Corresponding author

    CiNii

  • A Study on the Structure Analysis of a Logistics Information Model Based on UML

    今 剛士, 後藤正幸, 増井忠幸

    日本経営工学会論文誌   57 ( 3 ) 243 - 252  2006.08  [Refereed]

     View Summary

    In this paper, we propose a new method to analyze physical distribution models written using UML (Unified Modeling Language). Recently, the use of automated (information technology, IT) management systems for complex corporation activities and processes has been adopted by many companies. IT is, of course, a powerful tool for physical distribution processes. However, it is impossible to construct an effective system without practical knowledge. Moreover, it is sometimes difficult for system engineers constructing information systems to understand the practical knowledge and essence of activities in actual physical distribution processes. Sometimes workers are entirely ignorant when it comes to the use of information systems. From the background mentioned above, it is necessary to construct a good tool that enables both sides, the system engineers and distribution workers, to communicate and exchange information. UML is an efficient language to use for such communications. However, it is not easy to clarify complex UML models. We therefore introduce a method to analyze the structure of physical distribution described by UML and apply it to a standard logistics model. The proposal is based on multivariate analysis. Using this method, we can extract the structure of complex UML diagrams.

    DOI

  • An Evaluation of Joint Delivery System from the Viewpoint of Environmental Logistics

    Yumi Kurishima, Akihisa Tanda, Masayuki Goto, Tadayuki Masui

    The Proceedings of the 11th International Symposium on Logistics   ( ISL2006 ) 439 - 445  2006.07  [Refereed]

  • A Study on the Logistic System with Environmental Efficiency and Economic Effectiveness

    Masayuki Goto, Tadayuki Masui, Nobuyuki Kawai

    The Proceedings of the 11th International Symposium on Logistics   ( ISL2006 ) 432 - 438  2006.07  [Refereed]

    Authorship:Lead author

  • Kathmandu: Women Tackle Solid Waste Management

    Brenda Bushell, Masayuki Goto

    Women & Environment, Women and Urban Sustainability   No.70/71   60 - 62  2006.05  [Refereed]

    Authorship:Last author

  • インターネットを用いた大学間連携による遠隔授業の開発と評価

    後藤 正幸, 中澤 真, 湯田 亜紀, 三浦 円, 大野 昭彦, 萩原 拓郎

    武蔵工業大学環境情報学部, 情報メディアセンタージャーナル   7 ( 7 ) 6 - 13  2006.04

    Authorship:Lead author, Corresponding author

    CiNii

  • 環境英語を学ぶeラーニング教材開発とその評価

    吉田 国子, ブレンダ・ブッシェル, 後藤 正幸, 松元 崇子

    武蔵工業大学環境情報学部, 情報メディアセンタージャーナル   7 ( 7 ) 14 - 19  2006.04

    CiNii

  • 高齢者向けパソコン教室の設計と運営による実践的教育

    後藤 正幸, 中村 雅子, 倉田 宏子, 田中 愛子

    武蔵工業大学環境情報学部, 情報メディアセンタージャーナル   7 ( 7 ) 36 - 45  2006.04

    Authorship:Lead author, Corresponding author

    CiNii

  • The Nepal Project: Student-centered Learning as a Framework for Study Abroad

    Brenda Bushell, Masayuki Goto, Naomi Hara

    武蔵工業大学 環境情報学部 紀要   7   92 - 102  2006.02

  • A Study of Logistics Information System for Future Style of Physical Distribution

    後藤 正幸, 増井 忠幸, 今 剛士, 河合 伸幸

    日本設備管理学会誌   17 ( 4 ) 198 - 209  2006.02  [Refereed]

    Authorship:Lead author

    CiNii

  • ネパールと連携した環境教育コンテンツの構築による実践教育

    後藤 正幸, ブレンダ・ブッシェル, 原直美

    CIEC コンピュータ&エデュケーション   19   70 - 74  2005.12  [Refereed]

    Authorship:Lead author, Corresponding author

    DOI

  • オーストラリア熱帯雨林保全プログラムにおける環境教育と情報教育との相乗効果について

    後藤 正幸, 小堀 洋美

    CIEC コンピュータ&エデュケーション   18   57 - 68  2005.05  [Refereed]

    Authorship:Lead author

    DOI CiNii

  • 大学の情報系授業における学生アンケートの分析

    石田 崇, 後藤 正幸, 平澤 茂一

    CIEC コンピュータ&エデュケーション   18   152 - 157  2005.05  [Refereed]

    DOI CiNii

  • Construction of Education Modules: The Cyber Nepal Project

    Masayuki Goto, Brenda Bushell, Naomi Hara

    Journal of the Center for Information Studies   6 ( 6 ) 22 - 29  2005.04  [Refereed]

    Authorship:Lead author, Corresponding author

    CiNii

  • サイバー・オーストラリア熱帯雨林プロジェクトの実施とその教育効果

    後藤正幸, 小堀洋美

    武蔵工業大学環境情報学部, 情報メディアセンタージャーナル   6 ( 6 ) 10 - 21  2005.04

    Authorship:Lead author, Corresponding author

    CiNii

  • 初級プログラミング科目を対象とした学内遠隔教育とその効果

    後藤正幸, 大野明彦, 萩原拓郎, 横井利彰

    武蔵工業大学環境情報学部, 情報メディアセンタージャーナル   6 ( 6 ) 30 - 37  2005.04

    Authorship:Lead author, Corresponding author

    CiNii

  • リハビリテーション・データベース開発−Healthcare Qualityとリスク調整アウトカム評価−

    太田 久彦, 小林 順子, 木村 哲彦, 高倉 保幸, 陶山 哲夫, 高橋 邦泰, 後藤 正幸

    日本リハビリテーション病院施設協会誌   Vol.6   158 - 162  2004.12  [Refereed]

    Authorship:Last author

  • ネットワークを活用した中小企業の技術マーケティング

    後藤正幸, 増井忠幸, 渡邊法比古

    武蔵工業大学環境情報学部, 情報メディアセンタージャーナル   5 ( 5 ) 44 - 51  2004.03  [Invited]

    Authorship:Lead author, Corresponding author

    CiNii

  • 階層型意思決定モデル(AHP)と統計学的考察

    後藤正幸

    武蔵工業大学環境情報学部 紀要   5   77 - 88  2004.02

    Authorship:Lead author, Last author, Corresponding author

    CiNii

  • リハビリテーション診療データベースの開発(1) −身体機能・ADL評価−

    小林 順子, 太田 久彦, 木村 哲彦, 伊藤 高司, 後藤 正幸, 大久保 寛基, 大成 尚

    医療情報学   23   375 - 376  2003.11  [Refereed]

    J-GLOBAL

  • リハビリテーション診療データベースの開発(2) −治療の記述と「治療対効果」の分析−

    太田 久彦, 小林 順子, 木村 哲彦, 伊藤 高司, 後藤 正幸, 大久保 寛基, 大成 尚

    医療情報学   23   377 - 378  2003.11  [Refereed]

    J-GLOBAL

  • Representation method for a set of documents from the viewpoint of Bayesian statistics

    Masayuki Goto, Takashi Ishida, Shigeichi Hirasawa

    Proceedings of the IEEE International Conference on Systems, Man and Cybernetics   5 ( SMC2003 ) 4637 - 4642  2003.10  [Refereed]

    Authorship:Lead author

     View Summary

    In this paper, we consider the Bayesian approach for representation of a set of documents. In the field of representation of a set of documents, many previous models, such as the latent semantic analysis (LSA), the probabilistic latent semantic analysis (PLSA), the Semantic Aggregate Model (SAM), the Bayesian Latent Semantic Analysis (BLSA), and so on, were proposed. In this paper, we formulate the Bayes optimal solutions for estimation of parameters and selection of the dimension of the hidden latent class in these models and analyze it's asymptotic properties.

    DOI

  • A Source Model with Probability Distribution over Word Set and Recurrence Time Theorem

    Masayuki Goto, Toshiyasu Matsushima, Shigeichi Hirasawa

    IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences   E86-A ( 10 ) 2517 - 2525  2003.10  [Refereed]

    Authorship:Lead author

     View Summary

    Nishiara and Morita defined an i.i.d. word-valued source which is defined as a pair of an i.i.d. source with a countable alphabet and a function which transforms each symbol into a word over finite alphabet. They showed the asymptotic equipartition property (AEP) of the i.i.d. word-valued source and discussed the relation with source coding algorithm based on a string parsing approach. However, their model is restricted in the i.i.d. case and any universal code for a class of word-valued sources isn't discussed. In this paper, we generalize the i.i.d. word-valued source to the ergodic word-valued source which is defined by an ergodic source with a countable alphabet and a function from each symbol to a word. We show existence of entropy rate of the ergodic word-valued source and its formula. Moreover, we show the recurrence time theorem for the ergodic word-valued source with a finite alphabet. This result clarifies that Ziv-Lempel code (ZL77 code) is universal for the ergodic word-valued source.

  • 中小企業の新たな技術マーケティング

    渡邊 法比古, 後藤 正幸

    経営システム   13 ( 3 ) 109 - 113  2003.10  [Invited]

    Authorship:Last author

    CiNii

  • The Research of Decision Models That Production and Sales are Integrative for Production Switching

    森雅俊, 後藤正幸

    日本経営工学会論文誌   54 ( 1 ) 27 - 35  2003.04  [Refereed]

    Authorship:Last author

    DOI CiNii

  • On universality of both Bayes codes and Ziv-Lempel codes for sources which emit data sequence by block unit

    Takashi Ishida, Masayuki Gotoh, Shigeichi Hirasawa

    Electronics and Communications in Japan, Part III: Fundamental Electronic Science (English translation of Denshi Tsushin Gakkai Ronbunshi)   86 ( 1 ) 58 - 69  2003

     View Summary

    Ziv-Lempel (ZL) codes and Bayes codes are typical universal codes. An improved algorithm of the ZL code is widely used in compression software. On the other hand, practical use of Bayes codes is difficult due to the large amount of computation needed. However, a realizable algorithm in terms of computation effort has been constructed for the FSMX model group [9]. In this paper, an information source generating a sequence by word units is assumed as a model that can represent the probabilistic structure of actual data such as text data. The asymptotic compression performance of both codes is analyzed and evaluated for the information source class (information source for the block unit) with a constant (fixed) word length. As a result, it is found that Bayes code cannot directly be universal as a coding algorithm for symbol units. On the other hand, the ZL78 code can be directly universal. Also, a configuration method for the Bayes coding method is given for an information source with a block unit.

    DOI

    Scopus

    2
    Citation
    (Scopus)
  • A Study on Weight Estimation of Analytic Hierarchy Process Using the Weighted Least Squares Method

    村山直人, 後藤正幸, 俵 信彦

    日本経営工学会論文誌   53 ( 5 ) 368 - 377  2002.12  [Refereed]

    DOI

  • On Universality of Both Bayes Codes and Ziv-Lempel Codes for Sources which Emit Data Sequence by Block Unit

    石田崇, 後藤正幸, 平澤茂一

    電子情報通信学会論文誌 A   J84-A ( 9 ) 1167 - 1178  2001.09  [Refereed]

    CiNii

  • An Analysis of the Difference of Code Lengths Between Two-step Codes based on MDL Principle and Bayes Codes

    Masayuki Goto, Toshiyasu Matsushima, Shigeichi Hirasawa

    IEEE Transactions on Information Theory   47 ( 3 ) 927 - 944  2001.03  [Refereed]

    Authorship:Lead author, Corresponding author

     View Summary

    In this paper, we discuss the difference in code lengths between the code based on the minimum description length (MDL) principle (the MDL code) and the Bayes code under the condition that the same prior distribution is assumed for both codes. It is proved that the code length of the Bayes code is smaller than that of the MDL code by o(1) or O(1) for the discrete model class and by O(1) for the parametric model class. Because we can assume the same prior for the Bayes code as for the code based on the MDL principle, it is possible to construct the Bayes code with equal or smaller code length than the code based on the MDL principle. From the viewpoint of mean code length per symbol unit (compression rate), the Bayes code is asymptotically indistinguishable from the MDL two-stage codes.

    DOI

    Scopus

    7
    Citation
    (Scopus)
  • A study on difference of codelengths between codes based on MDL principle and bayes codes for given prior distributions

    Masayuki Gotoh, Toshiyasu Matsushima, Shigeichi Hirasawa

    Electronics and Communications in Japan, Part III: Fundamental Electronic Science (English translation of Denshi Tsushin Gakkai Ronbunshi)   84 ( 4 ) 30 - 40  2001

     View Summary

    The principle of the Minimum Description Length (MDL) proposed by J. Rissanen provides a type of structure for the model estimation based on probabilistic model selection allowing minimization of the codelength. On the other hand, the use of Bayes codes makes it possible to find a coding function from a mix of probabilistic models without specifying any concrete model. It has been pointed out that codes based on the MDL principle (MDL codes) are closely related to Bayes theory because in the definition of the description length of the probabilistic model, an unknown prior distribution is assumed. In this paper, we apply asymptotic analysis to the codelength difference between the MDL codes and Bayes codes, including cases of different prior distributions. The results of the analysis clearly show that in the case of discrete model families, codes having a high prior distribution in true models (that is, the models for which an advantageous prior distribution is assumed) are favorable, but in the case of parametric model families, Bayes codes have shorter codelength than the MDL codes even in the cases of advantageous prior distribution assumed for the MDL codes. © 2000 Scripta Tech-nica.

    DOI

    Scopus

  • An Analysis on Error Rate of Statistical Model Selection Based on Bayes Decision Theory

    後藤正幸, 松嶋敏泰, 平澤茂一

    日本経営工学会誌   50 ( 6 ) 639 - 650  2000.06  [Refereed]

    Authorship:Lead author

     View Summary

    Statistical model selection is one of the most important problems in statistics, and many works have left essential results. The conventional information criteria for model selection, such as the Akaike information criterion (AIC), the Bayesian information criterion (BIC), and minimum description length (MDL) were derived from different viewpoints. Many other model selection criteria have also been reported from various viewpoints. On the other hand, if we specify the model class and assume prior probabilities, then we can acquire Bayes optimal model selection for a finite number of samples based on Bayes decision theory. Furthermore, we can assume the various loss function adapting the purpose of model selection for practical cases. In this paper, we analyze the asymptotic properties of stasistical model selection based on Bayes decision theory. At first, we formulate Bayes optimal solution based on Bayes decision theory. In this formulation, we introduce the general loss function for practical problems. Moreover, we analyze the upper limits of the error rate of the model selection.

    DOI CiNii

  • Asymptotic Normality of Extended Posterior Density Functions with Loss Functions

    後藤正幸, 松嶋敏泰, 平澤茂一

    電子情報通信学会論文誌A   J83-A ( 6 ) 639 - 650  2000.06  [Refereed]

    Authorship:Lead author, Corresponding author

    CiNii

  • A Study on Bayes Optimal Prediction for Linear Regression Models

    鈴木友彦, 後藤正幸, 俵 信彦

    日本経営工学会誌   51 ( 1 ) 59 - 69  2000.04  [Refereed]

     View Summary

    In this paper, we propose an asymptotic Bayes optimal prediction algorithm for linear regression model, which reduces complexity in terms of calculation. In the field of industrial engineering, linear regression analyses are mainly applied to statistical quality control and demand forecasting, owing to effectiveness of control, prediction, analysis of structure, and so on. Recently, statistical model selection has been studied as a method of estimation for linear regression models, and applied to various problems of prediction. The statistical model selection is to select a particular model out of all candidates which include the true probabilistic model. The conventional criteria for model selection are F-value, FPE and information criteria; for example, AIC, BIC, and MDL. The mainly purposes of statistical model selection are to detect the true probability model, predict for future observations, and compress the data. Since statistical model selection has many applications, it has been studied not only in the field of statistics but also in various fields of science such as information theory, automatical control theory, and so on. In the case of estimation of the linear regression model by statistical model selection, generally, a particular model is selected by information criterion based on a previous observation from all candidates. In the linear regression analysis, the model class is a set of the combination of explanatory variables. However, in the case of prediction, it is not necessary to select a particular model. IN this case, the purpose of prediction is to acquire the accuracy estimator of the future observation. Therefore, previous studies using statistical model selection for prediction may be insufficient. On the other hand, the prediction method based on Bayes decision theory has been reported in various fields. In this method, predictions using the mixture model, which is mixing all candidates, are acquired as Bayes optimal solution, which minimizes the Bayesian mean square error. For this reason, we apply the mixture model for the linear regression models for prediction. We, at first, show that prediction by the mixture model is Bayes optimal prediction. However, it is difficult to strictly calculate the mixture probability because of the integration complexity on the parameter space. Therefore, we propose a new prediction method which removes the integration on account of reducing the complexity. Strictly speaking, we propose an asymptotic Bayes optimal prediction, which calculates the asymptotic posterior predictive distribution; i.e., mixture model. At last, we verify the effectiveness of the proposal through the simulation experiments.

    DOI CiNii

  • Almost sure and mean convergence of extended stochastic complexity

    Masayuki Gotoh, Toshiyasu Matsushima, Shigeichi Hirasawa

    IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences   E82-A ( 10 ) 2129 - 2134  1999.10  [Refereed]

    Authorship:Lead author, Corresponding author

     View Summary

    We analyse the extended stochastic complexity (ESC) which has been proposed by K. Yamanishi. The ESC can be applied to learning algorithms for on-line prediction and batch-learning settings. Yamanishi derived the upper bound of ESC satisfying uniformly for all data sequences and that of the asymptotic expectation of ESC. However, Yamanishi concentrates mainly on the worst case performance and the lower bound has not been derived. In this paper, we show some interesting properties of ESC which are similar to Bayesian statistics: the Bayes rule and the asymptotic normality. We then derive the asymptotic formula of ESC in the meaning of almost sure and mean convergence within an error of o(1) using these properties.

  • A Study of Periodical Ordering System to Control the Cost Occurred from Variances

    西島 淳, 後藤正幸, 俵 信彦

    日本経営工学会誌   50 ( 4 ) 207 - 215  1999.10  [Refereed]

     View Summary

    For production-inventory systems based on periodical ordering systems, it is important to control variances in order quantity and inventory. Therefore, a periodical ordering system based on optimal control theory has been proposed. The criterion of the ordering system is the weighted summation of those variances. The formula of order minimizes the criterion, and optimal order quantity can be calculated for every period. However, it is rational for the ordering system to minimize the cost function in practice. If variance in inventory is large, then a shortage or remainder of inventory would occur and inventory cost would rise. If variance in order quantity is large, then production loss and extra production costs would rise. In this paper, we formulate a periodical ordering system minimizing the cost function that depends on variances in order quantity and inventory. We show a method for searching the formula to calculate the optimal order quantity. This solution is based on optimal control theory for a linear system with colored noise.

    DOI CiNii

  • A Study of Parametric Estimation in Contingency Tables

    菊池淑子, 後藤正幸, 俵 信彦

    日本経営工学会誌   50 ( 3 ) 163 - 170  1999.08  [Refereed]

     View Summary

    In this paper, we consider the contingency table as a two-way classification with binomial distribution at each of the cells. The contingency table is useful for many practical cases in the field of the quality control, market research, and so on. In the analysis of data of the contingency table, the probabilistic models with parameters are assumed at each cell and the parameters are estimated from observations (data sets). In the case of binomial distribution, the parameters are the probabilities of each cell. The maximum likelihood estimator (MLE) for each cell is one of the parameter estimators. However, the precision of the MLE may not be sufficient when the sample size is small. On the other hand, if we previously assume the prior density on parameter space, we can formulate Bayes optimal estimation of the parameter with the square error loss function. This estimator is Bayes optimal for the finite sample. The another way for precise estimation is to estimate the parameter after the hypothesis, such that the parameter of some cell is equal to that of another cell. If the hypothesis is correct, the estimation error may be reduced rather than parameters being independently estimated at each cell. If we can previously assume a hypothetical set, a hypothesis may be selected from the observations, but in a true hypothesis, the set is unknown. In such a method, although we can regard a hypothesis as a model, this method can be formulated based on the statistical model selection problem, and we can apply conventional information criteria to select the model. However, in Bayes decision theory, the selection of a model is not optimal for prediction. We can formulate Bayes optimal estimator on the condition that the set of candidates of the models is given. In this paper, we propose a method to predict future observations and estimate the parameter values at each cell based on Bayes optimal solution, which uses a mixture model of all candidates. That is, we shall show that the method using the mixture model is Bayes optimal solution with the square error loss function for the parameter estimation similar to the prediction problem. Moreover, we shall show a practical algorithm to calculate Bayes optimal estimator using a mixture model assuming Bata distribution and uniform distribution as the prior distributions for the parameter space and model class, respectively. Through the simulation experiment, we shall show the properties of the proposed method for parameter estimation.

    DOI CiNii

  • A Study on Difference of Codelength between Codes based on MDL Principle and Bayes Codes for Given Prior Distributions

    Masayuki Goto, Toshiyasu Matsushima, Shigeichi Hirasawa

    電子情報通信学会論文誌 A   J82-A ( 5 ) 698 - 708  1999.05  [Refereed]

    Authorship:Lead author

  • A Note on Variable to Fixed-Length Codes without Error Propagation for Markov Sources

    Masaru Kimura, Masayuki Goto, Toshiyasu Matsushima, Shigeichi Hirasawa

    電子情報通信学会論文誌 A   J82-A ( 5 ) 736 - 741  1999.05  [Refereed]

    CiNii

  • Statistical Model Selection Based on Bayes Decision Theory and Its Application to Change Detection Problem

    Masayuki Gotoh, Shigeichi Hirasawa

    International Journal of Production Economics   60-61   629 - 638  1999.04  [Refereed]

    Authorship:Lead author

     View Summary

    The statistical model selection problems are discussed, and the criterion based on the Bayes decision theory for the detection of the true model is derived. We propose a new Bayesian model selection scheme, which can detect the change points of the parameter of the information source and also estimate each unknown parameter. In this paper, we formulate the Bayes optimal solution of the change detection problem, and analyze its property of the consistency.

    DOI

    Scopus

    1
    Citation
    (Scopus)
  • A Generalization of B. S. Clarke and A. R. Barron's asymptotics of bayes codes for FSMX sources

    Masayuki Gotoh, Toshiyasu Matsushima, Shigeichi Hirasawa

    IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences   E81-A ( 10 ) 2123 - 2132  1998.10  [Refereed]

    Authorship:Lead author, Corresponding author

     View Summary

    We shall generalize B.S. Clarke and A.R. Barron's analysis of the Bayes method for the FSMX sources. The FSMX source considered here is specified by the set of all states and its parameter value. At first, we show the asymptotic codelengths of individual sequences of the Bayes codes for the FSMX sources. Secondly, we show the asymptotic expected codelengths. The Bayesian posterior density and the maximum likelihood estimator satisfy asymptotic normality for the finite ergodic Markov source, and this is the key of our analysis.

  • Analysis of Push and Pull-Type Production Systems Characteristics in Terms of Difference in Ordering Cycle Periods

    加藤宏幸, 後藤正幸, 俵 信彦

    日本経営工学会誌   49 ( 3 ) 127 - 134  1998.09  [Refereed]

     View Summary

    With conventional models, in order to compare the properties of push-type production systems with those of pull-type systems, in previous works it was assumed that the ordering cycle period of the push-type system was equal to that of the pull-type system. The ordering cycle period of the push-type system is, however, usually different from that of the pull-type system in actual use. For example, the ordering cycles of the push and pull-type systems may be given every day and every three hours, respectively. The results of previous works with the same ordering cycle period cannot be applied to the above practical cases. In this paper we propose a new model in order to compare the properties of the push and pull-type systems under condition that the ordering cycle period may be different. The criteria in this paper are variances in inventory and order quantity. This is because we want to investigate the essence of the behaviors of the production systems. At first, we formulate optimal push and pull-type production systems. Secondly, we formulate a demand model in order to consider the differences in the ordering cycle periods, and apply this model to both systems. Finally, we evaluate the performance through numerical analysis. We analyze the rate of the ordering cycle period coinciding with the evaluated value of the push-type system with that of the pull-type system, which is denoted by τ^*. As the result, we show that the effect of τ to the variances is very strong. If autocorrelation of the demand is strong, then τ^* is large, however, if the autocorrelation is weak, then τ^* is smaller than 2.

    DOI CiNii

  • On Error Rates of Statistical Model Selection Based on Information Criteria

    Masayuki Gotoh, Toshiyasu Matsushima, Shigeichi Hirasawa

    IEEE International Symposium on Information Theory   ( IEEE ISIT98 ) 417  1998.08  [Refereed]

    Authorship:Lead author

     View Summary

    In this paper, we shall derive the upper bounds on error rates of the statistical model selection using the information criteria, P. Similar bounds were derived by J. Suzuki (1993). We shall generalize the results for the general model class. © 1998 IEEE.

    DOI

    Scopus

  • A generalization of B. S. Clarke and A. R. Barron's asymptotics of bayes codes for FSMX sources

    Masayuki Gotoh

    IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences   E81-A ( 10 ) 2123 - 2132  1998

     View Summary

    SUMMARY We shall generalize B. S. Clarke and A. R. Barren's analysis of the Bayes method for the FSMX sources. The FSMX source considered here is specified by the set of all states and its parameter value. At first, we show the asymptotic codelengths of individual sequences of the Bayes codes for the FSMX sources. Secondly, we show the asymptotic expected codelengths. The Bayesian posterior density and the maximum likelihood estimator satisfy asymptotic normality for the finite ergodic Markov source, and this is the key of our analysis.

  • A Study on a Method Improving Efficiency of Search Based on Conjugate Gradient Method

    吉田隆弘, 後藤正幸, 俵 信彦

    日本経営工学会誌   48 ( 5 ) 257 - 263  1997.12  [Refereed]

     View Summary

    BP (Back Propagation) learning algorithm based on Conjugate Gradient Method is an effective method for high-speed learning. This method has originally been introduced for the problem to minimize the quadratic function and is guaranteed to converge to it in the number of times equal to the search surface dimensions. It is also applied as it is to a generalized function like BP learning algorithm, because in this case too, it can approximate locally to a quadratic function. However, it fails to converge in the number of times equal to the search dimensions in this case, requiring the step to take the second approximation process after the search dimensions reached a certain number. This step is called "Restart". However, this method occasionally falls into local minimal depending on search surface, because it locally approximates a generalized function to a quadratic function. The cause is considered that it continues searching until its number of times reaches the conventionally determined number of times before the restart, even if the approximation to a quadratic function is low in precision ; therefore this restart method is considered to improve the search efficiency when the quadratic function approximation is low in precision. We therefore propose an efficient learning algorithm based on Conjugate Gradient Method. The method proposed here can decide whether the restart is needed according to new parameters which evaluate the quadratic approximation accuracy at every search point. We call the proposed method "An improved Conjugate Gradient Method".

    DOI CiNii

  • On Bayesian Optimal Prediction Based on Mixture Model of Multilayer Neural Networks

    橋川弘紀, 後藤正幸, 俵 信彦

    電子情報通信学会誌 D-II   80 D-II ( 7 ) 1919 - 1928  1997.07  [Refereed]

  • A Study on Difference of Codelengths between MDL Codes and Bayes Codes on Case Different Priors Are Assumed

    Masayuki Gotoh, Toshiyasu Matsushima, Shigeichi Hirasawa

    IEEE International Symposium on Information Theory   ( IEEE ISIT97 ) 403  1997.06  [Refereed]

    Authorship:Lead author

     View Summary

    We analyze quantitatively the the difference of codelengths between both codes for the hierarchical (nested) model class. © 1997 IEEE.

    DOI

    Scopus

  • Optimal Control for Linear System with Colored Noise and Its Application to Periodical Ordering System

    Masayuki Goto, Midori Uchizono, Nobuhiko Tawara

    日本経営工学会誌   47 ( 2 ) 107 - 116  1996.06  [Refereed]

    Authorship:Lead author, Corresponding author

     View Summary

    The objective of this paper is to formulate the optimal control system for the linear system with colored noise and to apply to the periodical ordering system for the push-type production and inventory systems, which provides the possible procedure to control both the variances of order quantity and inventory. This system realizes the optimization of the criterion of the weighted sum of the variances of production and inventory. For the extension to the solution of multi-stage process and of the case the demand is not stationary, it is necessary to formulate the control system by the optimal control theory. However, the optimal regulator control which is the conventional optimal control theory can be applied directly to the system with colored noise. In this paper, we propose the new formulation of this problem and the application to the production and inventory control system.

    DOI CiNii

  • A Formulation by Minimization of Differential Entropy for Optimal Control System

    Masayuki Goto, Shigeichi Hirasawa, Nobuhiko Tawara

    IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences   E79A ( 4 ) 569 - 577  1996.04  [Refereed]

    Authorship:Lead author

     View Summary

    This paper proposes a new formulation which minimizes the differential entropy for an optimal control problem. The conventional criterion of the optimal regulator control is a standard quadratic cost function E[M{x(t)}(2) + N{u(t)}(2)], where x(t) is a state variable, u(t) is an input value, and M and N are positive weights. However, increasing the number of the variables of the system it is complex to find the solution of the optimal regulator control. Therefore, the simplicity of the solution is required. In contrast to the optimal regulator control, we propose the minimum entropy control which minimizes a differential entropy of the weighted sum of x(t) and u(t). This solution is derived on the assumptions that the linear control and x(t)u(t) less than or equal to 0 are satisfied. As the result, the formula of the minimum entropy control is very simple and clear. This result will be useful for the further work with multi variables of simple control formulation.

  • FK型発注システムによる定期発注システムの統一的考察

    後藤正幸, 内園みどり, 俵 信彦

    日本経営工学会誌   46 ( 6 ) 565 - 572  1996.02  [Refereed]

    Authorship:Lead author, Corresponding author

    DOI

  • 最適レギュレータに基づく定期発注システムに関する研究

    田村嘉浩, 後藤正幸, 俵 信彦

    日本経営工学会誌   46 ( 6 ) 542 - 549  1996.02  [Refereed]

    DOI

  • A formulation by minimization of differential entropy for optimal control system

    Masayuki Gotoh, Nobuhiko Tawara

    IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences   E79-A ( 4 ) 569 - 575  1996

     View Summary

    This paper proposes a new formulation which minimizes the differential entropy for an optimal control problem. The conventional criterion of the optimal regulator control is a standard quadratic cost function E[M{x(t)}2 + N{u(t)}2], where x(t) is a state variable, u(t) is an input value, and M and N are positive weights. However, increasing the number of the variables of the system it is complex to find the solution of the optimal regulator control. Therefore, the simplicity of the solution is required. In contrast to the optimal regulator control, we propose the minimum entropy control which minimizes a differential entropy of the weighted sum of x(t) and u(t). This solution is derived on the assumptions that the linear control and x(t)u(t) ≦ 0 are satisfied. As the result, the formula of the minimum entropy control is very simple and clear. This result will be useful for the further work with multi variables of simple control formulation.

  • 変傾共役勾配法によるBP学習の安定化と高速化

    後藤正幸, 開沼泰隆, 俵 信彦

    日本経営工学会誌   46 ( 2 ) 152 - 158  1995.06  [Refereed]

    Authorship:Lead author, Corresponding author

    DOI CiNii

  • 共役勾配法を導入したBP学習における安定化に関する研究

    後藤正幸, 俵 信彦

    日本経営工学会誌   46 ( 1 ) 70 - 77  1995.04  [Refereed]

    Authorship:Lead author, Corresponding author

    DOI

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Books and Other Publications

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Presentations

  • 決定木と信頼上界を用いた文脈付きバンディットアルゴリズム手法の提案

    大岩 将, 阿部 太一, 木村 恵悟, 鈴木 佐俊, 後藤 正幸

    第38回人工知能学会全国大会(JSAI2024) 

    Presentation date: 2024.05

    Event date:
    2024.05
     
     
  • 不確実性が高い事象の確率的予測と解釈を可能とするGaussian-SAINTの提案

    更家 崚介, 磯村 時将, 清水 良太郎, 後藤 正幸

    第38回人工知能学会全国大会(JSAI2024) 

    Presentation date: 2024.05

    Event date:
    2024.05
     
     
  • Twin-G NeuMF:ノイズに頑健なNeural Matrix Factorizationの改良モデル

    長命 祥吾, 松岡 龍汰, 清水 成, 楊 添翔, 後藤 正幸

    第38回人工知能学会全国大会(JSAI2024) 

    Presentation date: 2024.05

    Event date:
    2024.05
     
     
  • 大規模画像言語モデルを用いた領域埋め込みによる画像分類手法に関する一考察

    櫻井 洸介, 石井 達也, 清水 良太郎, 宋 林鑫, 後藤 正幸

    第38回人工知能学会全国大会(JSAI2024) 

    Presentation date: 2024.05

    Event date:
    2024.05
     
     
  • One Dimensional-CNNとデータ拡張に基づくセンシング点群データからの動作識別モデル

    鈴木 一央, 水谷 美穂, 山田 晃輝, 山極 綾子, 後藤 正幸

    第38回人工知能学会全国大会(JSAI2024) 

    Presentation date: 2024.05

    Event date:
    2024.05
     
     
  • 一対比較DNNに基づく商品画像評価値推定における評価者の主観差の分析

    山極 綾子, 後藤 正幸

    第38回人工知能学会全国大会(JSAI2024) 

    Presentation date: 2024.05

    Event date:
    2024.05
     
     
  • Knowledge Graph Attention Networkに基づく購買行動の多様性を考慮した顧客分析手法の提案

    藤原 大喜, 森川 卓哉, 山極 綾子, 後藤 正幸

    第38回人工知能学会全国大会(JSAI2024) 

    Presentation date: 2024.05

    Event date:
    2024.05
     
     
  • 異なる粒度が混在する教師データに適応した階層型マルチラベル分類モデル

    宮島 健悟, 布目 悠人, 阪井 優太, 後藤 正幸

    第38回人工知能学会全国大会(JSAI2024) 

    Presentation date: 2024.05

    Event date:
    2024.05
     
     
  • トピックモデルと品質要素分類に基づく単語観点でのユーザレビュー分析手法

    小笠原 のりこ, 泓 亜由乃, 山極 綾子, 後藤 正幸

    第38回人工知能学会全国大会(JSAI2024) 

    Presentation date: 2024.05

    Event date:
    2024.05
     
     
  • プロンプト学習を用いた複数ドメイン適応画像言語モデルの精度向上法

    高 振宇, 山極 綾子, 後藤 正幸

    第38回人工知能学会全国大会(JSAI2024) 

    Presentation date: 2024.05

    Event date:
    2024.05
     
     
  • マルチタスク学習に基づく極端な気象タグの予測アルゴリズムに関する一考察

    天野 智貴, 清水 良太郎, 後藤 正幸, 吉開 朋弘

    第46回情報理論とその応用シンポジウム (SITA2023) 

    Presentation date: 2023.12

    Event date:
    2023.11
    -
    2023.12
  • クエリ指向要約モデルを用いたレビュー分析手法に関する一考察

    中村 太祐, 阪井 優太, 後藤 正幸

    第46回情報理論とその応用シンポジウム (SITA2023) 

    Presentation date: 2023.12

    Event date:
    2023.11
    -
    2023.12
  • 施策効果の高い顧客グループの特定を目的とした機械学習に基づく実験計画手法

    中村 友香, 山極 綾子, 佐々木 北都, 後藤 正幸

    第46回情報理論とその応用シンポジウム (SITA2023) 

    Presentation date: 2023.11

    Event date:
    2023.11
    -
    2023.12
  • 機械学習に基づく複数種類のクーポン配布施策の実験計画および効果検証モデル

    米田 安希子, 清水 良太郎, 桜井 詩音, 川田 心, 山下 遥, 後藤 正幸

    第46回情報理論とその応用シンポジウム (SITA2023) 

    Presentation date: 2023.11

    Event date:
    2023.11
    -
    2023.12
  • Self- and Semi-Supervised Learning に基づく行動履歴データに対する分析モデルにおける一考察

    竹内 瑞生, 阪井 優太, 後藤 正幸

    第46回情報理論とその応用シンポジウム (SITA2023) 

    Presentation date: 2023.11

    Event date:
    2023.11
    -
    2023.12
  • 重要度サンプリングを用いた敵対的反事実回帰モデルの提案

    今福 太一, 阪井 優太, 後藤 正幸

    第46回情報理論とその応用シンポジウム (SITA2023) 

    Presentation date: 2023.11

    Event date:
    2023.11
    -
    2023.12
  • 機械学習に基づく中古スマートフォン端末の将来価格予測モデルに関する一考察

    増田 雅樹, 山極 綾子, 後藤 正幸

    第46回情報理論とその応用シンポジウム (SITA2023) 

    Presentation date: 2023.11

    Event date:
    2023.11
    -
    2023.12
  • 角度に基づいた高次元データ可視化手法に関する一考察

    阪井 優太, 三川 健太, 後藤 正幸

    第46回情報理論とその応用シンポジウム (SITA2023) 

    Presentation date: 2023.11

    Event date:
    2023.11
    -
    2023.12
  • 求人データに対する適切な職種情報付与のためのラベル修正アルゴリズム

    山田 晃輝, 山極 綾子, 後藤 正幸

    第46回情報理論とその応用シンポジウム (SITA2023) 

    Presentation date: 2023.11

    Event date:
    2023.11
    -
    2023.12
  • 宿泊施設を対象としたBERTと自動翻訳に基づく多言語レビューの埋め込み表現モデル

    森本 貫太, 楊 添翔, 後藤 正幸

    第46回情報理論とその応用シンポジウム (SITA2023) 

    Presentation date: 2023.11

    Event date:
    2023.11
    -
    2023.12
  • 顧客の嗜好と商品画像特徴量の関係性を推定する新たな潜在クラスモデルの提案

    土屋 希琳, 清水 良太郎, 後藤 正幸

    第46回情報理論とその応用シンポジウム (SITA2023) 

    Presentation date: 2023.11

    Event date:
    2023.11
    -
    2023.12
  • Neural Matrix Factorization に基づく予測モデルの汎化性能の改良に関 する一考察

    長命祥吾, 松岡龍汰, 清水成, 楊添翔, 後藤正幸

    日本計算機統計学会 第37回シンポジウム(宮崎県宮崎市) 

    Presentation date: 2023.11

    Event date:
    2023.11
     
     
  • 領域埋め込みを用いた画像言語モデルによる未観測ドメインの画像分類手法の 提案

    櫻井洸介, 石井達也, 清水良太郎, 宋林鑫, 後藤正幸

    日本計算機統計学会 第37回シンポジウム(宮崎県宮崎市) 

    Presentation date: 2023.11

    Event date:
    2023.11
     
     
  • 評価値との関連に着目した商品レビュー分析のための感情語抽出手法

    小笠原のりこ, 泓亜由乃, 山極綾子, 後藤正幸

    日本計算機統計学会 第37回シンポジウム(宮崎県宮崎市) 

    Presentation date: 2023.11

    Event date:
    2023.11
     
     
  • 埋込空間を利用した顧客の購買行動とレビューの分析

    布目悠人, 阪井優太, 後藤正幸

    日本計算機統計学会 第37回シンポジウム(宮崎県宮崎市) 

    Presentation date: 2023.11

    Event date:
    2023.11
     
     
  • 明示的評価を用いた埋め込み表現によるクラスタリング手法

    水谷 美穂, 山極 綾子, 後藤 正幸

    日本経営工学会 2023年秋季大会 

    Presentation date: 2023.10

    Event date:
    2023.10
     
     
  • 外部ドメインデータを用いた転移学習のための効率的なデータ選択に関する研究

    三好観悠, 清水良太郎, 宋林鑫, 後藤正幸

    日本経営工学会 2023年秋季大会 

    Presentation date: 2023.10

    Event date:
    2023.10
     
     
  • A Consideration about A Sparse Estimation Method for Polynomial Regression Model with Unknown Maximum Degree

    Kazuma Inoue, Ryotaro Shimizu, Tota Suko, Masayuki Goto

    IPSJ SIG Technical Report 

    Presentation date: 2023.06

  • 段階的パフォーマンス向上を目的とした人事評価データ分析

    浅野正和, 山極綾子, 後藤正幸

    経営情報学会 2023年度年次大会 

    Presentation date: 2023.06

    Event date:
    2023.06
     
     
  • 主幹電力データからの家電製品の時系列状態推定モデル

    鈴木佐俊, 小林 学, 後藤正幸

    経営情報学会 2023年度年次大会 

    Presentation date: 2023.06

    Event date:
    2023.06
     
     
  • Applications and Further Developments of a Fashion Intelligence System for Fashion-Specific Ambiguous Expressions Interpretation

    Ryotaro Shimizu, Yuki Saito, Masayuki Goto

    The 37th Annual Conference of the Japanese Society for Artificial Intelligence (JSAI2023) 

    Presentation date: 2023.06

    Event date:
    2023.06
     
     
  • Proposal of Product Image Generation Model based on CVAE Learning Abstract Information of Text Data

    Ayuno Fuchi, Masaki Masuda, Masakazu Asano, Ayako Yamagiwa, Masayuki Goto

    The 37th Annual Conference of the Japanese Society for Artificial Intelligence (JSAI2023) 

    Presentation date: 2023.06

    Event date:
    2023.06
     
     
  • Factor analysis model for improving evaluation value by BERT and SHAP for review text data

    Mamiko Watanabe, Koki Yamada, Ryotaro Shimizu, Satoshi Suzuki, Masayuki Goto

    The Annual Conference of the Japanese Society for Artificial Intelligence (JSAI2023) 

    Presentation date: 2023.06

    Event date:
    2023.06
     
     
  • Price factor analysis model for used smartphone products based on machine learning

    Takuya Morikawa, Mizuki Takeuchi, Yuta Sakai, Masayuki Goto

    The Annual Conference of the Japanese Society for Artificial Intelligence (JSAI2023) 

    Presentation date: 2023.06

    Event date:
    2023.06
     
     
  • A Study on Performance and Efficiency Improvement of FT-Transformer

    Tokimasa Isomura, Tomoki Amano, Ryotaro Shimizu, Masayuki Goto

    The 37th Annual Conference of the Japanese Society for Artificial Intelligence (JSAI2023) 

    Presentation date: 2023.06

    Event date:
    2023.06
     
     
  • Adversarial Training with Data Selection which Improves the Accuracy and Reduces the Computational Complexity of Domain Adaptation

    Keigo Kimura, Daisuke Nakamura, Yuta Sakai, Goto Masayuki

    The 37th Annual Conference of the Japanese Society for Artificial Intelligence (JSAI2023) 

    Presentation date: 2023.06

    Event date:
    2023.06
     
     
  • An Effectiveness Verification Model for Coupon Distribution Measures Based on Machine Learning Considered Users' Purchase Intention

    Akiko Yoneda, Ryotaro Shimizu, Shion Sakurai, Makoto Kawata, Haruka Yamashita, Masayuki Goto

    The 37th Annual Conference of the Japanese Society for Artificial Intelligence (JSAI2023) 

    Presentation date: 2023.06

    Event date:
    2023.06
     
     
  • 顧客と商品の補助情報を考慮したAttention機構付きEmbeddingモデルによる顧客特性分析

    石井達也, 土屋希琳, 楊 添翔, 後藤正幸

    第37回人工知能学会全国大会(JSAI2023) 

    Presentation date: 2023.06

    Event date:
    2023.06
     
     
  • Proposal of customer segmentation method based on the impact of each feature on outcome variable

    Naru Shimizu, Yuka Nakamura, Ayako Yamagiwa, Masayuki Goto

    The 37th Annual Conference of the Japanese Society for Artificial Intelligence (JSAI2023) 

    Presentation date: 2023.06

    Event date:
    2023.06
     
     
  • 順序関係のある複数の暗黙的評価を活用したNCRモデルによる推薦モデルに関する一考察

    松岡龍汰, 米田 安希子, 山下 遥, 後藤正幸

    第37回人工知能学会全国大会(JSAI2023) 

    Presentation date: 2023.06

    Event date:
    2023.06
     
     
  • A Study on Selection Bias Correction Based on Statistical Decision Theory in Logistic Regression Models

    Taichi Abe, Tota Suko, Masayuki Goto

    The 37th Annual Conference of the Japanese Society for Artificial Intelligence (JSAI2023) 

    Presentation date: 2023.06

    Event date:
    2023.06
     
     
  • Follow up survey on the effect of the education program through Nepal field applying the concept of active learning

    Haruka Yamashita, Manita Shresta, Sugihara Masaaki, Masayuki Goto

    Japan Society for Educational Technology, 2023 Spring Conference 

    Presentation date: 2023.03

    Event date:
    2023.03
     
     
  • Analysis of the internal effects of collaborative education programs with Japanese universities on the university students in Nepal

    Manita Shrestha, Haruka Yamashita, Sugihara Masaaki, Masayuki Goto

    Japan Society for Educational Technology, 2023 Spring Conference 

    Presentation date: 2023.03

    Event date:
    2023.03
     
     
  • 弱教師あり学習におけるラベル修正のためのクエリアルゴリズムに関する一考察

    宋 林シン, 楊 添翔, 後藤正幸

    第45回情報理論とその応用シンポジウム (SITA2022) 

    Presentation date: 2022.12

    Event date:
    2022.11
    -
    2022.12
  • ミニバッチ学習によるスケーラブルな隠れセミマルコフモデルの推定手法に関する一考察

    高尾洋佑, 山極綾子, 山下 遥, 後藤正幸

    第45回情報理論とその応用シンポジウム (SITA2022) 

    Presentation date: 2022.12

    Event date:
    2022.11
    -
    2022.12
  • 入力依存の分散を考慮したベイズ最適化によるビジネス施策決定モデルの提案

    良川太河, 阪井優太, 楊 添翔, 後藤正幸

    第45回情報理論とその応用シンポジウム (SITA2022) 

    Presentation date: 2022.12

    Event date:
    2022.11
    -
    2022.12
  • トピック分布を活用した文脈付きバンディットアルゴリズムによる施策決定法

    松苗亮汰, 山極綾子, 後藤正幸

    第45回情報理論とその応用シンポジウム (SITA2022) 

    Presentation date: 2022.12

    Event date:
    2022.11
    -
    2022.12
  • SHAP値を活用した機械学習による店舗販売データに基づく商品間の関係性分析モデルに関する一考察

    石倉滉大, 阪井優太, 吉開朋弘, 後藤正幸

    第45回情報理論とその応用シンポジウム (SITA2022) 

    Presentation date: 2022.12

    Event date:
    2022.11
    -
    2022.12
  • 複数のECマーケティング施策を対象とした観察データに基づく効果推定手法

    坪井優樹, 阪井優太, 清水良太郎, 後藤正幸

    第45回情報理論とその応用シンポジウム (SITA2022) 

    Presentation date: 2022.12

    Event date:
    2022.11
    -
    2022.12
  • BERTの特徴量抽出に基づく製品レビュー分析モデルに関する一考察

    山下皓太郎, 山極綾子, 蓮本恭輔, 後藤正幸

    第45回情報理論とその応用シンポジウム (SITA2022) 

    Presentation date: 2022.12

    Event date:
    2022.11
    -
    2022.12
  • クレジットカードへの予測切り替え期間を用いたユーザ分析モデルに関する一考察

    大久保亮吾, 阪井優太, 立花徹也, 長坂典香, 後藤正幸

    第45回情報理論とその応用シンポジウム (SITA2022) 

    Presentation date: 2022.11

    Event date:
    2022.11
    -
    2022.12
  • 知識グラフと強化学習に基づく説明可能な推薦のための効率的な経路探索アルゴリズム

    楊 冠宇, 清水良太郎, 山極綾子, 後藤正幸

    第45回情報理論とその応用シンポジウム (SITA2022) 

    Presentation date: 2022.11

    Event date:
    2022.11
    -
    2022.12
  • 多様な画像ランキングを出力可能なDNNモデルの構築法に関する一考察

    山極綾子, 後藤正幸

    日本計算機統計学会 第36回シンポジウム(富山国際会議場) 

    Presentation date: 2022.11

    Event date:
    2022.11
     
     
  • 多言語レビューによる宿泊施設の海外旅行者向けのサービス品質向上に関する研究

    森本貫太, 楊 添翔, 後藤正幸

    日本経営工学会秋季大会 

    Presentation date: 2022.11

    Event date:
    2022.11
     
     
  • 埋め込み空間における推薦領域を考慮した推薦アイテム獲得手法の提案

    天野智貴, 清水良太郎, 後藤正幸

    日本経営工学会秋季大会 

    Presentation date: 2022.11

    Event date:
    2022.11
     
     
  • Hawkes過程を用いた暗号資産の取引状況分析手法に関する一考察

    増田雅樹, 山極綾子, 後藤正幸

    日本経営工学会秋季大会 

    Presentation date: 2022.11

    Event date:
    2022.11
     
     
  • 潜在的な関係性の違いを考慮した知識グラフによる推薦システムの一考察

    中村太祐, 阪井優太, 後藤正幸

    日本経営工学会秋季大会 

    Presentation date: 2022.11

    Event date:
    2022.11
     
     
  • 機械学習に基づくファッション特有の曖昧な表現を自動的に解釈するためのシステム

    清水良太郎, 斎藤侑輝, 松谷 恵, 後藤正幸

    日本経営工学会秋季大会 

    Presentation date: 2022.11

    Event date:
    2022.11
     
     
  • 選択データを用いた敵対的訓練によるドメイン適応に関する一考察

    木村恵悟, 坪井優樹, 阪井優太, 後藤正幸

    日本計算機統計学会 第36回シンポジウム(富山国際会議場) 

    Presentation date: 2022.11

    Event date:
    2022.11
     
     
  • 離反確率に基づく施策対象ユーザ選定手法に関する一考察

    中村友香, 佐々木北都, 後藤正幸

    日本計算機統計学会 第36回シンポジウム(富山国際会議場) 

    Presentation date: 2022.11

    Event date:
    2022.11
     
     
  • CNNを用いた能動学習におけるラベル付与データの選択手法に関する一考察

    益田恵里花, 山田晃輝, 清水良太郎, 後藤正幸

    日本計算機統計学会 第36回シンポジウム(富山国際会議場) 

    Presentation date: 2022.11

    Event date:
    2022.11
     
     
  • SHAP値を用いたクラスタリングによる顧客セグメンテーション手法の提案

    清水 成, 中村友香, 山極綾子, 後藤正幸

    日本計算機統計学会 第36回シンポジウム(富山国際会議場) 

    Presentation date: 2022.11

    Event date:
    2022.11
     
     
  • 多大学連携型オンライン反転ゼミの設計とその教育効果

    後藤正幸, 小林 学, 守口 剛, 関 庸 一, 鈴木秀男, 生田目 崇, 中田和秀, 石垣 綾, 上田雅夫, 佐藤公俊, 三川健太, 山下 遥, 田尻 裕

    第21回情報科学技術フォーラム(FIT2022) 

    Presentation date: 2022.09

    Event date:
    2022.09
     
     
  • 反転ゼミ形式による多大学で連携するオンライン研究交流の試み ― データサイエンス領域のオンラインゼミを事例として ―

    後藤正幸, 小林 学, 守口 剛, 関 庸 一, 鈴木秀男, 生田目 崇, 中田和秀, 石垣 綾, 上田雅夫, 佐藤公俊, 三川健太, 山下 遥, 田尻 裕

    2022 PCカンファレンス 

    Presentation date: 2022.08

    Event date:
    2022.08
     
     
  • 強調データの拡張学習によるBiterm Topic Model の解釈性向上法に関する一考察

    西田有輝, 楊 添翔, 山下 遥, 後藤正幸

    第36回人工知能学会全国大会(JSAI2022), 3E3-GS-2-02 

    Presentation date: 2022.06

    Event date:
    2022.06
     
     
  • 目的関数値の悪化を抑制するベイズ最適化に基づくオンライン学習に関する一考察

    中村友香, 良川太河, 山極綾子, 後藤正幸

    第36回人工知能学会全国大会(JSAI2022) ,2C5-GS-2-05 

    Presentation date: 2022.06

    Event date:
    2022.06
     
     
  • 専用アプリ上の質問データに基づく子育てライフステージの課題変化分析に関する一考察

    山田晃輝, 山極綾子, 高尾洋佑, 後藤正幸

    第36回人工知能学会全国大会(JSAI2022), 2L1-GS-2-01 

    Presentation date: 2022.06

    Event date:
    2022.06
     
     
  • Collaborative Metric Learningに基づく推薦リストの意外性向上手法に関する一考察

    米田 安希子, 松苗亮汰, 山下 遥, 後藤正幸

    第36回人工知能学会全国大会(JSAI2022), 1A4-GS-2-03 

    Presentation date: 2022.06

    Event date:
    2022.06
     
     
  • Ladder Networkによる半教師有り学習に基づくユーザ属性予測モデルに関する一考察

    竹内瑞生, 今福太一, 阪井優太, 後藤正幸

    第36回人工知能学会全国大会(JSAI2022), 1A4-GS-2-01 

    Presentation date: 2022.06

    Event date:
    2022.06
     
     
  • 隠れセミマルコフモデルに基づくユーザの興味持続性を考慮したアイテム分析手法に関する一考察

    土屋希琳, 坪井優樹, 清水 良太郎, 後藤 正幸

    第36回人工知能学会全国大会(JSAI2022), 1A4-GS-2-04 

    Presentation date: 2022.06

    Event date:
    2022.06
     
     
  • BERTによる特徴抽出を駆使した商品レビュー分析モデルに関する一考察

    山下皓太郎, 雲居玄道, 蓮本恭輔, 後藤正幸

    情報処理学会第84回全国大会 

    Presentation date: 2022.03

    Event date:
    2022.03
     
     
  • ベイズ最適化に基づく最適パラメータ探索法の研究動向と課題に関する一考察

    良川太河, 小林 学, 後藤正幸

    情報処理学会第84回全国大会 

    Presentation date: 2022.03

    Event date:
    2022.03
     
     
  • 構成的符号化を用いたECOCの一構成法(続)

    平澤茂一, 雲居玄道, 八木秀樹, 小林 学, 後藤正幸, 稲積宏誠

    情報処理学会第84回全国大会 

    Presentation date: 2022.03

    Event date:
    2022.03
     
     
  • ハウスホルダーフローを導入したEmbedded Topic Modelに関する一考察

    松苗亮汰, 山極綾子, 後藤正幸

    情報処理学会第84回全国大会 

    Presentation date: 2022.03

    Event date:
    2022.03
     
     
  • 複数の施策を対象とした処置効果推定手法に関する一考察

    坪井優樹, 阪井優太, 清水良太郎, 後藤正幸

    情報処理学会第84回全国大会 

    Presentation date: 2022.03

    Event date:
    2022.03
     
     
  • 回帰・分類問題における能動学習の研究動向と課題に関する一考察

    阪井優太, 小林 学, 後藤正幸

    情報処理学会第84回全国大会 

    Presentation date: 2022.03

    Event date:
    2022.03
     
     
  • 購買アイテムを特定する分類器パラメータを用いた商品分析モデルに関する一考察

    山極綾子, 後藤正幸

    情報処理学会第84回全国大会 

    Presentation date: 2022.03

    Event date:
    2022.03
     
     
  • Visualization Analysis of Relationships between Employees Focusing on Content of Communication on Business Chat

    Tatsuya Kawakami, Haruka Yamashita, Hajime Hotta, Masayuki Goto

    Presentation date: 2021.12

    Event date:
    2021.12
     
     
  • 購買履歴と画像情報を共学習するCVAEに基づく生花の新商品イメージ自動生成手法

    北里 礼, 雲居玄道, 後藤正幸

    第44回情報理論とその応用シンポジウム(SITA2021) 

    Presentation date: 2021.12

    Event date:
    2021.12
     
     
  • 複数の商品購買順序情報を考慮する拡張Translation-based Recommendationモデルの提案

    李 ア舒, 山極綾子, 楊 添翔, 後藤正幸

    第44回情報理論とその応用シンポジウム(SITA2021) 

    Presentation date: 2021.12

    Event date:
    2021.12
     
     
  • 2段階の機械学習予測モデルに基づく季節性中古ファッションアイテムの需要予測に関する一考察

    齊藤芙佑, 山下 遥, 佐々木 北都, 後藤正幸

    第44回情報理論とその応用シンポジウム(SITA2021) 

    Presentation date: 2021.12

    Event date:
    2021.12
     
     
  • Webサイトの閲覧行動分析のための時間窓トピックモデルの提案

    伊藤史世, 雲居玄道, 後藤正幸

    第44回情報理論とその応用シンポジウム(SITA2021) 

    Presentation date: 2021.12

    Event date:
    2021.12
     
     
  • 売り切れ情報を考慮したマルチタスク学習に基づく惣菜アイテムの需要予測モデルに関する一考察

    上原諒介, 雲居玄道, 後藤正幸, 吉開朋弘

    第44回情報理論とその応用シンポジウム(SITA2021) 

    Presentation date: 2021.12

    Event date:
    2021.12
     
     
  • 二値分類器の推定誤差に基づく多値分類性能に関する一考察

    雲居玄道, 八木秀樹, 小林 学, 後藤正幸, 平澤 茂一

    第44回情報理論とその応用シンポジウム(SITA2021) 

    Presentation date: 2021.12

    Event date:
    2021.12
     
     
  • ビジネスチャット上の会話内容に着目した社員間の関係性の可視化分析

    川上達也, 山下遥, 堀田創, 後藤正幸

    日本経営工学会秋季大会 

    Presentation date: 2021.11

    Event date:
    2021.11
     
     
  • 売り切れを考慮した小売チェーン店の需要予測に関する分析

    上原諒介, 雲居玄道, 後藤正幸, 吉開朋弘

    日本経営工学会秋季大会 

    Presentation date: 2021.11

    Event date:
    2021.11
     
     
  • ユーザの嗜好とネットワーク構造を考慮した行動分析モデルに関するー考察

    宋 林シン, 齊藤芙佑, 山下 遥, 後藤正幸

    第35回人工知能学会全国大会(JSAI2021) 

    Presentation date: 2021.06

    Event date:
    2021.06
     
     
  • 分散表現を用いたレシピの多様性の分析モデルに関する一考察

    山下 皓太郎, 伊藤史世, 蓮本恭輔, 後藤正幸

    第35回人工知能学会全国大会(JSAI2021) 

    Presentation date: 2021.06

    Event date:
    2021.06
     
     
  • 識別クラスごとに学習した生成モデルに基づく分布外検知に関する一考察

    松苗亮汰, 齊藤芙佑, 山下 遥, 後藤正幸

    第35回人工知能学会全国大会(JSAI2021) 

    Presentation date: 2021.06

    Event date:
    2021.06
     
     
  • ノードのマルチラベル分類を可能にするSelf-Attention Networkの拡張モデル

    飯塚玲夫, 川上達也, 楊 添翔, 後藤正幸

    第35回人工知能学会全国大会(JSAI2021) 

    Presentation date: 2021.06

    Event date:
    2021.06
     
     
  • マルチラベル分類における Deep Neural Networkの共有構造の構築法に関する一考察

    石倉滉大, 北里 礼, 雲居玄道, 後藤正幸

    第35回人工知能学会全国大会(JSAI2021) 

    Presentation date: 2021.06

    Event date:
    2021.06
     
     
  • アンサンブル学習の予測性を保持する単一決定木構築アルゴリズム

    良川太河, 山極綾子, 楊 添翔, 後藤正幸

    第35回人工知能学会全国大会(JSAI2021) 

    Presentation date: 2021.06

    Event date:
    2021.06
     
     
  • Causal Treeに基づく選択バイアスを考慮した条件付き平均処置効果推定手法に関する一考察

    坪井優樹, 阪井優太, 鈴木佐俊, 後藤正幸

    第35回人工知能学会全国大会(JSAI2021) 

    Presentation date: 2021.06

    Event date:
    2021.06
     
     
  • 個別介入効果を評価する商品推薦モデルに関する考察

    今福太一, 川上達也, 楊 添翔, 後藤正幸

    第35回人工知能学会全国大会(JSAI2021) 

    Presentation date: 2021.06

    Event date:
    2021.06
     
     
  • 出品履歴データを学習した Robust Variational Autoencoderの潜在表現による店舗分析

    大久保 亮吾, 上原諒介, 雲居玄道, 後藤正幸, 吉開朋弘

    第35回人工知能学会全国大会(JSAI2021) 

    Presentation date: 2021.06

    Event date:
    2021.06
     
     
  • 人事評価の数理モデルと評価値推定法

    後藤正幸, 山極 毅

    日本経営工学会春季大会 

    Presentation date: 2021.05

    Event date:
    2021.05
     
     
  • メトリックラーニングにおける潜在構造モデルと潜在変数の推定に関する一考察

    三川健太, 小林 学, 後藤正幸, 平澤茂一

    日本経営工学会春季大会 

    Presentation date: 2021.05

    Event date:
    2021.05
     
     
  • 構成的符号化を用いたECOCの一構成法

    雲居玄道, 平澤茂一, 八木秀樹, 小林 学, 後藤正幸

    日本経営工学会春季大会 

    Presentation date: 2021.05

    Event date:
    2021.05
     
     
  • 動く教材で学ぶデータエンジニアリング

    雲居玄道, 八木秀樹, 後藤正幸

    日本経営工学会春季大会 

    Presentation date: 2021.05

    Event date:
    2021.05
     
     
  • ファッション系ECサイトにおける多様な補助情報を有したグラフ構造の学習アルゴリズムに関する一考察

    清水良太郎, 松谷 恵, 後藤正幸

    日本経営工学会春季大会 

    Presentation date: 2021.05

    Event date:
    2021.05
     
     
  • 生花ECサイトを対象とした閲覧履歴に基づく購買行動分析に関する一考察

    楊 添翔, 鎌形祐志, 山極綾子, 後藤正幸

    日本経営工学会春季大会 

    Presentation date: 2021.05

    Event date:
    2021.05
     
     
  • トピックへの所属確率分布を考慮した学術論文へのキーワードの割り当て手法

    阪井優太, 浅見 怜, 後藤正幸

    日本経営工学会春季大会 

    Presentation date: 2021.05

    Event date:
    2021.05
     
     
  • 解釈性を有するアンサンブル識別機の効率的な学習法に関する一考察

    良川太河, 山極綾子, 楊 添翔, 後藤正幸

    日本経営工学会春季大会 

    Presentation date: 2021.05

    Event date:
    2021.05
     
     
  • 分散表現モデルに基づく料理レシピの多様性分析手法

    山下皓太郎, 伊藤史世, 蓮本恭輔, 後藤正幸

    日本経営工学会春季大会 

    Presentation date: 2021.05

    Event date:
    2021.05
     
     
  • 複数店舗を有する小売チェーンにおける代替商品検出アルゴリズムの提案

    上原諒介, 雲居玄道, 後藤正幸, 吉開朋弘

    日本経営工学会春季大会 

    Presentation date: 2021.05

    Event date:
    2021.05
     
     
  • マルチラベル分類におけるDeep Neural Network のノード共有学習法

    石倉滉大, 北里 礼, 雲居玄道, 後藤正幸

    日本経営工学会春季大会 

    Presentation date: 2021.05

    Event date:
    2021.05
     
     
  • Robust Variational Autoencoderの潜在表現による店舗分析モデルに関する一考察

    大久保亮吾, 上原諒介, 雲居玄道, 後藤正幸, 吉開朋弘

    日本経営工学会春季大会 

    Presentation date: 2021.05

    Event date:
    2021.05
     
     
  • 選択バイアスを考慮するCausal Treeによる条件付き平均処置効果推定手法

    坪井優樹, 阪井優太, 鈴木佐俊, 後藤正幸

    日本経営工学会春季大会 

    Presentation date: 2021.05

    Event date:
    2021.05
     
     
  • 識別クラスごとの生成モデルに基づく尤度比を用いた分布外検知に関する一考察

    松苗亮汰, 齊藤芙佑, 山下 遥, 後藤正幸

    日本経営工学会春季大会 

    Presentation date: 2021.05

    Event date:
    2021.05
     
     
  • Self-Attention Networkを用いたノードのマルチラベル分類

    飯塚玲夫, 川上達也, 楊 添翔, 後藤正幸

    日本経営工学会春季大会 

    Presentation date: 2021.05

    Event date:
    2021.05
     
     
  • 深層異常検知モデルの中間表現によるデータ分析手法に関する一考察

    北里 礼, 相木将寛, 雲居玄道, 後藤正幸

    日本経営工学会春季大会 

    Presentation date: 2021.05

    Event date:
    2021.05
     
     
  • EC サイト上の購買行動における顧客嗜好変化の分析手法に関する一考察

    李 ア舒, 山極綾子, 楊 添翔, 後藤正幸

    日本経営工学会春季大会 

    Presentation date: 2021.05

    Event date:
    2021.05
     
     
  • 仮想通貨取引データに対する敵対的生成ネットワークを用いた分類性能向上手法の検討

    楊 冠宇, 清水良太郎, 雲居玄道, 後藤正幸

    日本経営工学会春季大会 

    Presentation date: 2021.05

    Event date:
    2021.05
     
     
  • ユーザの嗜好とネットワーク構造を考慮した行動分析モデルに関するー考察

    宋 林シン, 齊藤芙佑, 山下 遥, 後藤正幸

    日本経営工学会春季大会 

    Presentation date: 2021.05

    Event date:
    2021.05
     
     
  • 埋め込み空間上のトピック分布を考慮したアイテム推薦モデルに関する一考察

    齊藤芙佑, 小野雄生, 山下 遥, 後藤正幸

    日本経営工学会春季大会 

    Presentation date: 2021.05

    Event date:
    2021.05
     
     
  • トピックの階層性を考慮した購買行動分析モデルに関する一考察

    松岡佑以, 平野洋介, 阪井優太, 後藤正幸

    日本経営工学会春季大会 

    Presentation date: 2021.05

    Event date:
    2021.05
     
     
  • 生花ECサイトの購買履歴に基づく商品特性分析モデル

    Ayako Yamagiwa, Gendo Kumoi, Masayuki Goto

    DEIM2021 

    Presentation date: 2021.03

    Event date:
    2021.03
     
     
  • 潜在クラスモデルに基づくビジネスチャットアプリ上の従業員コミュニケーション分析

    Fuyu Saito, Ayako Yamagiwa, Tianxiang Yang, Masayuki Goto

    DEIM2021 

    Presentation date: 2021.03

    Event date:
    2021.03
     
     
  • ユーザ行動分析のための Knowledge Graph Attention Networkの拡張に関する一考察

    Fumiyo Ito, Zhiying Zhang, Gendo Kumoi, Masayuki Goto  [Invited]

    DEIM2021 

    Presentation date: 2021.03

    Event date:
    2021.03
     
     
  • 機械学習アプローチに基づく中古ファッションアイテムの価格保持期間の適正化モデルと実証的効果検証

    Izumi Kuwata, Kenta Mikawa, Masayuki Goto, Hokuto Sasaki

    DEIM2021 

    Presentation date: 2021.03

    Event date:
    2021.03
     
     
  • Zero-shot Generative Model Considering Attribute Uncertainty

    Yuta Sakai, Kenta Mikawa, Masayuki Goto

    Technical Report of IEICE, PRMU2020-59 

    Presentation date: 2020.12

    Event date:
    2020.12
    -
     
  • ビジネスデータを対象としたデータアナリティクスの現状と今後の展望

    後藤正幸  [Invited]

    電子情報通信学会 情報理論研究会(若手研究者のための講演会) 

    Presentation date: 2020.12

    Event date:
    2020.12
     
     
  • 混合正規分布を用いた合算計測値からの状態推定モデルについての一考察

    鈴木佐俊, 後藤正幸

    日本計算機統計学会 第34回シンポジウム 

    Presentation date: 2020.11

    Event date:
    2020.11
     
     
  • 天気予報情報を活用した全球数値予報モデルの可視化手法に関する一考察

    松元琢真, 雲居玄道, 後藤正幸, 吉開朋弘

    日本計算機統計学会 第34回シンポジウム 

    Presentation date: 2020.11

    Event date:
    2020.11
     
     
  • 社員間コミュニケーションの類型化を可能とするグラフ構造の分散表現モデル

    野中賢也, 山下遥, 堀田創, 後藤正幸

    日本計算機統計学会 第34回シンポジウム 

    Presentation date: 2020.11

    Event date:
    2020.11
     
     
  • 顧客成長のための施策立案を導く特徴転移型クラスタリングモデルの提案

    平野洋介, 楊添翔, 雲居玄道, 阿部 永, 立花徹也, 後藤正幸

    日本計算機統計学会 第34回シンポジウム 

    Presentation date: 2020.11

    Event date:
    2020.11
     
     
  • ターゲットマーケティングのためのWeb閲覧履歴による属性ラベル学習モデル

    青木章悟, 三川健太, 後藤正幸

    日本計算機統計学会 第34回シンポジウム 

    Presentation date: 2020.11

    Event date:
    2020.11
     
     
  • 生花ECサイトの閲覧履歴データを学習する改良型Latent LSTM Allocationモデルの提案

    張志穎, 楊 添翔, 後藤正幸

    日本計算機統計学会 第34回シンポジウム 

    Presentation date: 2020.11

    Event date:
    2020.11
     
     
  • 属性情報の不確実性を考慮したゼロショット生成モデル

    阪井優太, 三川健太, 後藤正幸

    第23回情報論的学習理論ワークショップ(IBIS2020) 

    Presentation date: 2020.11

    Event date:
    2020.11
     
     
  • Knowledge Graph Attention Networkに基づく購買行動分析モデルに関する一考察

    伊藤史世, 張 志穎, 雲居玄道, 後藤正幸

    国立情報学研究所 情報学研究データリポジトリIDRユーザフォーラム(IDRユーザフォーラム2020) 

    Presentation date: 2020.11

    Event date:
    2020.11
    -
     
  • 天気予報文作成支援のための位置情報付き自動タギング手法に関する一考察

    松元琢真, 雲居玄道, 後藤正幸, 吉開朋弘

    日本経営工学会秋季大会 

    Presentation date: 2020.10

    Event date:
    2020.10
     
     
  • ターゲットマーケティングのためのWeb閲覧行動データに基づく属性選択とその評価法

    青木章悟, 三川健太, 後藤正幸

    日本経営工学会秋季大会 

    Presentation date: 2020.10

    Event date:
    2020.10
     
     
  • 低頻度購買商品を対象とした分散表現モデリングに関する一考察

    山極綾子, 楊 添翔, 後藤正幸

    日本経営工学会秋季大会 

    Presentation date: 2020.10

    Event date:
    2020.10
     
     
  • 社員間のコミュニケーションデータから構築されたグラフ構造の分散表現モデル

    野中賢也, 山下 遥, 堀田 創, 後藤正幸

    日本経営工学会秋季大会 

    Presentation date: 2020.10

    Event date:
    2020.10
     
     
  • クレジット切替施策検討のためのポイントカードユーザ分析モデル

    平野洋介, 楊 添翔, 雲居玄道, 後藤正幸, 高橋雅信, 阿部 永, 立花徹也

    日本経営工学会秋季大会 

    Presentation date: 2020.10

    Event date:
    2020.10
     
     
  • LSTMモデルに基づくECサイトの閲覧履歴データ予測モデルに関する一考察

    張 志穎, 楊 添翔, 後藤正幸

    日本経営工学会秋季大会 

    Presentation date: 2020.10

    Event date:
    2020.10
     
     
  • 代表元を用いた計量距離学習におけるオンライン学習法に関する一考察

    三川健太, 小林 学, 後藤正幸, 平澤茂一

    日本経営工学会秋季大会 

    Presentation date: 2020.10

    Event date:
    2020.10
     
     
  • 生花通信配達事業におけるイベント特性を考慮した顧客の購買行動分析モデル

    北里 礼, 野中賢也, 山下 遥, 後藤正幸

    第19回情報科学技術フォーラム(FIT2020) 

    Presentation date: 2020.09

    Event date:
    2020.09
     
     
  • 機械学習アプローチに基づく中古ファッションアイテムの価格保持期間の適正化モデルと実証的効果検証

    桑田 和, 三川健太, 後藤正幸, 佐々木北都

    第19回情報科学技術フォーラム(FIT2020) 

    Presentation date: 2020.09

    Event date:
    2020.09
     
     
  • 外部条件を考慮した小売店における商品別売り上げの要因分析モデル

    平野洋介, 御供信薫, 楊 添翔, 後藤正幸, 吉開朋弘

    第19回情報科学技術フォーラム(FIT2020) 

    Presentation date: 2020.09

    Event date:
    2020.09
     
     
  • A Study for Response Interval Between User’s Communications on Business Chat Systems based on Latent Class Model

    Fuyu Saito, Ayako Yamagiwa, Tianxiang Yang, Masayuki Goto

    JSAI2020 

    Presentation date: 2020.06

    Event date:
    2020.06
     
     
  • Improved Meta-learning by Parameter Adjustment via Latent Variables and Probabilistic Inference

    Eiki Shimizu, Shogo Aoki, Kenta Mikawa, Masayuki Goto

    JSAI2020 

    Presentation date: 2020.06

    Event date:
    2020.06
     
     
  • A Prediction Model of Earned Runs Based on Latent Class Markov Chain for Starters of Professional Baseball Pitchers

    Ryosuke Uehara, Takuma Matsumoto, Kenta Mikawa, Masayuki Goto

    JSAI2020 

    Presentation date: 2020.06

    Event date:
    2020.06
     
     
  • A Study on Analysis Model of Customers' Purchasing Behavior Based on Knowledge Graph Attention Network

    Fumiyo Ito, Zhiying Zhang, Gendo Kumoi, Masayuki Goto

    JSAI2020 

    Presentation date: 2020.06

    Event date:
    2020.06
     
     
  • Construction of Demand Forecast Model of Tokyo Taxi Based on Probe Data Analysis

    Reo Iizuka, Yuki Ono, Kenya Nonaka, Yuta Sakai, Masayuki Goto

    JSAI2020 

    Presentation date: 2020.06

    Event date:
    2020.06
     
     
  • A Decision Model of Sales Period on List Price for Second-hand Fashion Items Based on Machine Learning Approach

    Izumi Kuwata, Shimpei Kanazawa, Kenta Mikawa, Masayuki Goto, Hokuto Sasaki

    JSAI2020 

    Presentation date: 2020.06

    Event date:
    2020.06
     
     
  • Network Analysis Between Emplyees Based on Business Chat Data

    Kenya Nonaka, Haruka Yamashita, Masayuki Goto

    JSAI2020 

    Presentation date: 2020.06

    Event date:
    2020.06
     
     
  • A Analytical Model of the Customer Purchase Factor Based on Conditional VAE Learned of Web Browsing Data

    Tatsuya Kawakami, Yuta Sakai, Haruka Yamashita, Masayuki Goto

    JSAI2020 

    Presentation date: 2020.06

    Event date:
    2020.06
     
     
  • A Study on Construction of Deep Neural Networks Based on Autoencoders for Each Category

    Kotaro Imai, Ryosuke Goto, Gendo Kumoi, Masayuki Goto

    JSAI2020 

    Presentation date: 2020.06

    Event date:
    2020.06
     
     
  • 質問・回答文書のトピック関連度を考慮したQAシステムモデル

    大川順也, 雲居玄道, 後藤正幸

    第42回情報理論とその応用シンポジウム(SITA2019) 

    Presentation date: 2019.11

  • 中古ファッションECサイトに出品価格と販売価格の関係分析モデルに関する一考察

    金澤真平, 楊 添翔, 後藤正幸

    第42回情報理論とその応用シンポジウム(SITA2019) 

    Presentation date: 2019.11

  • Hidden Topic Markov Modelsに基づく顧客購買行動分析に関する一考察

    保戸田未桜, 雲居玄道, 後藤正幸

    第42回情報理論とその応用シンポジウム(SITA2019) 

    Presentation date: 2019.11

  • 膨大な種類のアイテムを考慮した消費者購買行動の分析モデルに関する一考察

    安井一貴, 三川健太, 後藤正幸

    第42回情報理論とその応用シンポジウム(SITA2019) 

    Presentation date: 2019.11

  • 閲覧の遷移行動を考慮した分散表現に基づくWebサイトの関係分析モデル

    保坂大樹, 山下 遥, 後藤正幸

    第42回情報理論とその応用シンポジウム(SITA2019) 

    Presentation date: 2019.11

  • 多機能クレジットカードの利用履歴データに対する潜在クラスモデル分析

    世古裕都, 雲居玄道, 後藤正幸, 立花徹也

    第42回情報理論とその応用シンポジウム(SITA2019) 

    Presentation date: 2019.11

  • 電子商店街における店舗情報を考慮した商品推薦に関する研究

    大堀祐一, 楊 添翔, 山下 遥, 後藤正幸

    第42回情報理論とその応用シンポジウム(SITA2019) 

    Presentation date: 2019.11

  • ビジネスチャットアプリ上の会話履歴データを対象としたトピック分析モデル

    山極綾子, 世古裕都, 楊 添翔, 後藤正幸

    第42回情報理論とその応用シンポジウム(SITA2019) 

    Presentation date: 2019.11

  • 真の因果構造が未知の場合の因果効果の推定精度について

    井上一磨, 雲居玄道, 堀井俊佑, 須子統太, 後藤正幸

    第42回情報理論とその応用シンポジウム(SITA2019) 

    Presentation date: 2019.11

  • データの潜在的特徴を考慮したFactorization Machines に関する一考察

    杉崎智哉, 三川健太, 後藤正幸

    第42回情報理論とその応用シンポジウム(SITA2019) 

    Presentation date: 2019.11

  • 深層学習におけるデータ拡張による汎化性能の向上に関する研究

    藤波英輝, 雲居玄道, 後藤正幸

    第42回情報理論とその応用シンポジウム(SITA2019) 

    Presentation date: 2019.11

  • ランク学習モデルを用いた料理画像の魅力度定量化に関する一考察

    莫 鈞貽, 藤波英輝, 三川健太, 雲居玄道, 後藤正幸

    第22回情報論的学習理論ワークショップ (IBIS 2019) 

    Presentation date: 2019.11

  • Web閲覧履歴に基づくユーザクラスタリングにおける各潜在クラスモデルの挙動分析

    青木章悟, 三川健太, 後藤正幸

    第22回情報論的学習理論ワークショップ (IBIS 2019) 

    Presentation date: 2019.11

  • Slackの会話履歴データに基づく社員間ネットワーク分析モデル

    野中賢也, 山下 遥, 後藤正幸

    第22回情報論的学習理論ワークショップ (IBIS 2019) 

    Presentation date: 2019.11

  • 購買履歴に基づくポイントカードユーザの クレジット切り替え分析モデル

    平野洋介, 世古裕都, 楊 添翔, 後藤正幸, 立花徹也

    第22回情報論的学習理論ワークショップ (IBIS 2019) 

    Presentation date: 2019.11

  • LDAを用いた多様性を考慮する推薦システムに関する一考察

    張 志穎, 保坂大樹, 山下 遥, 後藤正幸

    人工知能学会 第33回全国大会 

    Presentation date: 2019.06

  • 被評価数を考慮した重み付き最小二乗法によるEM-NMFアンサンブル手法

    大堀祐一, 山下 遥, 後藤正幸

    人工知能学会 第33回全国大会 

    Presentation date: 2019.06

  • 評価傾向の差異を考慮した分散表現による協調フィルタリング

    後藤亮介, 藤波 英輝, 楊 添翔, 後藤正幸

    人工知能学会 第33回全国大会 

    Presentation date: 2019.06

  • 半教師ありブースティングの多値分類への拡張法

    阪井優太, 安井一貴, 三川健太, 後藤正幸

    人工知能学会 第33回全国大会 

    Presentation date: 2019.06

  • 販売履歴データに基づく中古ファッションアイテムの出品価格推定モデルの提案

    桑田 和, 杉崎智哉, 三川健太, 後藤正幸

    人工知能学会 第33回全国大会 

    Presentation date: 2019.06

  • 潜在表現モデルに基づくテレビ番組の魅力度要因分析モデル

    西村祐樹, 金澤真平, 楊 添翔, 後藤正幸

    人工知能学会 第33回全国大会 

    Presentation date: 2019.06

  • 天気予報文作成支援のためのテキスト分析モデルに関する研究

    松元琢真, 世古裕都, 雲居玄道, 吉開朋弘, 後藤正幸

    日本気象学会 2019年春季大会 

    Presentation date: 2019.05

  • 概況文作成支援のための数値予報類似度算出法

    雲居玄道, 後藤正幸, 吉開朋弘

    日本気象学会 2019年春季大会 

    Presentation date: 2019.05

  • 時系列ログデータにおける特殊データの検出に関する研究

    張 笑エン, 山下 遥, 雲居玄道, 後藤正幸

    第41回情報理論とその応用シンポジウム(SITA2018) 

    Presentation date: 2018.12

  • 購買データにおけるRFM指標の生成モデルのパラメータ推定について

    西尾友里, 山下 遥, 後藤正幸

    第41回情報理論とその応用シンポジウム(SITA2018) 

    Presentation date: 2018.12

  • 問合せ文書の分類先の偏りに注目した自動文書分類に関する研究

    大窪啓介, 雲居玄道, 後藤正幸

    第41回情報理論とその応用シンポジウム(SITA2018) 

    Presentation date: 2018.12

  • Group Lasso NMFに基づくグラフ構造推定法

    河部瞭太, 山下 遥, 後藤正幸

    第41回情報理論とその応用シンポジウム(SITA2018) 

    Presentation date: 2018.12

  • クレジットとポイントを併用可能な多機能クレジットカードにおける利用履歴データの統合分析モデルの提案

    清水良太郎, 山下 遥, 上田雅夫, 田中藍奈, 後藤正幸

    第41回情報理論とその応用シンポジウム(SITA2018) 

    Presentation date: 2018.12

  • 顧客分類を目的とした多次元時系列データからの特徴量選択法

    雲居玄道, 後藤正幸

    第41回情報理論とその応用シンポジウム(SITA2018) 

    Presentation date: 2018.12

  • 潜在的ディリクレ配分法を用いた問合せ文書と回答文書の関係分析

    大川順也, 雲居玄道, 後藤正幸

    第21回情報論的学習理論ワークショップ 

    Presentation date: 2018.11

  • 概況文作成支援のための予想天気図類似度算出法

    雲居玄道, 後藤正幸, 吉開朋弘

    第21回情報論的学習理論ワークショップ 

    Presentation date: 2018.11

  • 最大次数が未知の多項式回帰モデルに対するスパース推定に関する一考察

    井上一磨, 清水良太郎, 須子統太, 後藤正幸

    第21回情報論的学習理論ワークショップ 

    Presentation date: 2018.11

  • 人工データによる2値判別器を用いた多値分類システムの評価

    平澤茂一, 雲居玄道, 小林 学, 後藤正幸, 稲積宏誠

    経営情報学会 PACIS2018 主催記念特別全国研究発表大会 

    Presentation date: 2018.06

  • l1正則化に基づくFactorization Machineに関する一考察

    三川健太, 小林 学, 後藤正幸, 平澤茂一

    日本経営工学会 平成30年度春季大会 

    Presentation date: 2018.05

  • 会員ステージ制における顧客の行動分析のための転移学習モデルについて

    楊 添翔, 雲居玄道, 山下 遙, 後藤正幸

    日本経営工学会 平成30年度春季大会 

    Presentation date: 2018.05

  • 混合雑音を持つ回帰モデルの推定アルゴリズムとその応用

    後藤正幸

    日本経営工学会 平成30年度春季大会 

    Presentation date: 2018.05

  • 自己符号化器の中間表現を用いた特徴分析に関する一考察

    金澤真平, 杉山裕貴, 楊 添翔, 後藤正幸

    日本経営工学会 平成30年度春季大会 

    Presentation date: 2018.05

  • 特徴間の交互作用を考慮した学生ユーザの企業エントリー行動分析モデルに関する一考察

    杉崎智哉, 西尾友里, 三川健太, 後藤正幸, 桜井 崇

    日本経営工学会 平成30年度春季大会 

    Presentation date: 2018.05

  • EC サイトにおけるページ遷移順序を考慮した購買行動分析

    保戸田 未桜, 水落洋貴, 雲居玄道, 後藤正幸

    日本経営工学会 平成30年度春季大会 

    Presentation date: 2018.05

  • 周期性とイベント効果に着目した消費者の購買行動分析モデルに関する一考察

    安井一貴, 中野修平, 三川健太, 後藤正幸

    日本経営工学会 平成30年度春季大会 

    Presentation date: 2018.05

  • 気象条件と店舗特性を考慮した商品別需要モデル構築に関する一考察

    世古裕都, 清水 良太郎, 雲居玄道, 後藤正幸, 吉開朋弘

    日本経営工学会 平成30年度春季大会 

    Presentation date: 2018.05

  • 意味空間上の分布表現に基づくWebサイトと閲覧ユーザの統合分析モデル

    保坂大樹, 河部瞭太, 山下 遙, 後藤正幸

    日本経営工学会 平成30年度春季大会 

    Presentation date: 2018.05

  • 評価値とタグ情報の共起を表現する潜在クラスモデルによる協調フィルタリング

    大堀祐一, 河部瞭太, 張 笑エン, 山下 遙, 後藤正幸

    日本経営工学会 平成30年度春季大会 

    Presentation date: 2018.05

  • 美容サービス事業のKPIと顧客属性の関係分析 〜経営性能向上の施策提案を目指して〜

    新井浩健, 片山 博, 河部 瞭太, 山下 遙, 後藤正幸

    日本経営工学会 平成30年度春季大会 

    Presentation date: 2018.05

  • Tweetデータに基づく料理画像の魅力度定量化モデル

    藤波英輝, 清水 良太郎, 雲居玄道, 後藤正幸

    日本経営工学会 平成30年度春季大会 

    Presentation date: 2018.05

  • 非負値行列因子分解による顧客購買パターン抽出と顧客生涯価値予測

    蓮本恭輔, 後藤正幸, 雲居玄道

    情報処理学会第80回全国大会 

    Presentation date: 2018.03

  • ECサイトにおける購買履歴データとアンケートデータを融合した顧客の購買行動分析モデルの提案

    清水良太郎, 坂元哲平, 山下 遥, 後藤正幸

    日本計算機統計学会 第31回シンポジウム 

    Presentation date: 2017.11

  • ロジスティック分布における層別変数がある場合のベイズ最適な予測法

    荒井琢充, 三川健太, 後藤正幸

    日本計算機統計学会 第31回シンポジウム 

    Presentation date: 2017.11

  • グルメサービスにおける投稿データと獲得リアクション数の関係分析のための潜在クラスモデル

    坂元哲平, 山下 遥, 後藤正幸, 岩永二郎

    日本計算機統計学会 第31回シンポジウム 

    Presentation date: 2017.11

  • 中古ファッションアイテムの販売価格予測モデルを用いた価格設定に関する一考察

    仁ノ平将人, 三川健太, 後藤正幸

    日本計算機統計学会 第31回シンポジウム 

    Presentation date: 2017.11

  • ロジスティック回帰モデル族に対するベイズ最適予測アルゴリズム

    荒井琢充, 三川健太, 後藤正幸

    第40回情報理論とその応用シンポジウム(SITA2017) 

    Presentation date: 2017.11

  • 非負値テンソル因子分解に基づく気象条件と購買パターンの関係解析モデル

    岡山 成, 山下 遥, 三川健太, 後藤正幸, 吉開朋弘

    第40回情報理論とその応用シンポジウム(SITA2017) 

    Presentation date: 2017.11

  • ネットワーク分析に基づく顧客成長のための重要商品の抽出手法に関する一考察

    伊藤寛彬, 雲居玄道, 後藤正幸

    第40回情報理論とその応用シンポジウム(SITA2017) 

    Presentation date: 2017.11

  • 潜在クラスモデルに基づく投稿データと獲得リアクション数の関係分析モデルに対する一考察

    坂元哲平, 山下 遥, 後藤正幸, 岩永二郎

    第40回情報理論とその応用シンポジウム(SITA2017) 

    Presentation date: 2017.11

  • 混合回帰モデルに基づく中古ファッションアイテムの販売価格予測モデルの提案

    仁ノ平 将人, 三川健太, 後藤正幸

    第40回情報理論とその応用シンポジウム(SITA2017) 

    Presentation date: 2017.11

  • 人工データを用いた誤り訂正符号に基づく多値分類法における符号語表構成に関する一考察

    雲居玄道, 三川健太, 八木秀樹, 後藤正幸, 平澤茂一

    第40回情報理論とその応用シンポジウム(SITA2017) 

    Presentation date: 2017.11

  • Collaborative Filtering Based on the Latent Class Model Using Variational Bayes

    Manabu Kobayashi, Kenta Mikawa, Masayuki Goto, Shigeichi Hirasawa

    第40回情報理論とその応用シンポジウム(SITA2017) 

    Presentation date: 2017.11

  • 符号理論に基づく多値文書分類における二値判別器の相関に着目した符号語構成法

    雲居玄道, 八木秀樹, 後藤正幸, 平澤茂一

    第16回情報科学技術フォーラム(FIT2017) 

    Presentation date: 2017.09

  • FFDを用いた二値分類のための次元削減法に関する一考察

    大窪啓介, 雲居玄道, 後藤正幸

    情報処理学会 第115回数理モデル化と問題解決研究発表会 

    Presentation date: 2017.09

  • 符号理論の観点による二値判別器の相関に着目した多値文書分類のための符号語構成法

    雲居玄道, 八木秀樹, 後藤正幸, 平澤茂一

    情報処理学会 第115回数理モデル化と問題解決研究発表会 

    Presentation date: 2017.09

  • 局所的構造に着目したアンサンブル学習の分類精度向上に関する研究

    業天大貴, 仁ノ平 将人, 三川健太, 後藤 正幸

    日本経営工学会 平成29年度春季大会 

    Presentation date: 2017.05

  • 潜在的な購買傾向を考慮した顧客の会員ステージ向上モデルの提案

    西尾友里, 伊藤寛彬, 山下 遙, 後藤 正幸

    日本経営工学会 平成29年度春季大会 

    Presentation date: 2017.05

  • 少数の正例とラベルなし文書を用いた半教師付き学習に関する一考察

    水落洋貴, 岡山 成, 雲居玄道, 後藤正幸

    日本経営工学会 平成29年度春季大会 

    Presentation date: 2017.05

  • アンケートデータを考慮したECサイトの購買履歴分析モデルの提案

    清水 良太郎, 坂元哲平, 山下 遙, 後藤 正幸

    日本経営工学会 平成29年度春季大会 

    Presentation date: 2017.05

  • 購買行動分析のためのベイジアンネットワークの階層的構造学習の提案

    河部瞭太, 伊藤寛彬, 山下 遙, 後藤正幸

    日本経営工学会 平成29年度春季大会 

    Presentation date: 2017.05

  • ネパールにおけるICT活用の現状と展望に関する一考察

    新井浩健, マニタ・シュレスタ, 山下 遙, 後藤正幸, ブレンダ・ブッシェル

    日本経営工学会 平成29年度春季大会 

    Presentation date: 2017.05

  • ネパールにおける教育意識に関するフィールド分析

    浅田 愛, 後藤正幸, ブレンダ・ブッシェル, 山下 遙, マニタ・シュレスタ

    日本経営工学会 平成29年度春季大会 

    Presentation date: 2017.05

  • コールドスタート問題を考慮した推薦システムに関する一考察

    張 笑エン, 岡山 成, 山下 遙, 後藤正幸

    日本経営工学会 平成29年度春季大会 

    Presentation date: 2017.05

  • 就職ポータルサイトにおける個社ページ閲覧とエントリーの関係分析モデルに関する一考察

    杉山裕貴, 荒井琢充, 楊 添翔, 後藤正幸, 荻原 大陸

    日本経営工学会 平成29年度春季大会 

    Presentation date: 2017.05

  • Scratchを用いたプログラミング教育における学習者の思考パターン分析

    中澤真, 梅澤克之, 後藤正幸, 平澤茂一

    情報処理学会第79回全国大会 

    Presentation date: 2017.03

  • 2値判別器を用いた多値分類方式のシステム評価(続)

    平澤茂一, 雲居玄道, 小林 学, 後藤正幸, 稲積宏誠

    情報処理学会第79回全国大会 

    Presentation date: 2017.03

  • 二値判別器の性能に着目したECOC法による多値文書分類における符号語構成に関する一考察

    雲居玄道, 八木秀樹, 後藤正幸, 平澤茂一

    情報処理学会第79回全国大会 

    Presentation date: 2017.03

  • 非負値行列因子分解に基づく気象条件と商品売上パターンの関係分析モデルに関する一考察

    岡山 成, 山下 遥, 三川健太, 後藤正幸, 吉開朋弘

    情報処理学会第79回全国大会 

    Presentation date: 2017.03

  • 顧客の成長に着目したネットワーク分析による重要商品の抽出に関する一考察

    伊藤寛彬, 雲居玄道, 山下 遥, 後藤正幸

    情報処理学会第79回全国大会 

    Presentation date: 2017.03

  • 会員ステージ向上に着目した重要商品の分析手法に関するー考察

    楊 添翔, 山下 遥, 後藤正幸

    情報処理学会第79回全国大会 

    Presentation date: 2017.03

  • ニューラルネットワークによる非負値行列因子分解の表現とその拡張

    小林 学, 三川健太, 後藤正幸, 平澤茂一

    電子情報通信学会2017年総合大会 

    Presentation date: 2017.03

  • 「Scratch」を用いたプログラミング学習時の閲覧履歴・編集履歴・脳波履歴を組み合わせた学習者分析

    中澤真, 梅澤克之, 後藤正幸, 平澤茂一

    情報処理学会 コンピュータと教育研究会 138回研究発表会 

    Presentation date: 2017.02

  • 気象情報とTweetデータの統合的分析による体感気温の定量化とその需要予測への利用に関する一考察

    馬賀嵩士, 三川健太, 後藤正幸, 吉開朋弘

    第39回情報理論とその応用シンポジウム(SITA2016) 

    Presentation date: 2016.12

  • 混合回帰に基づく就職ポータルサイトの被エントリ数予測モデルの提案

    永森誠矢, 山下 遥, 後藤正幸, 荻原大陸

    第39回情報理論とその応用シンポジウム(SITA2016) 

    Presentation date: 2016.12

  • LDAに基づく未観測なカテゴリを含む文書集合の自動分類手法の提案

    山本祐生, 三川健太, 後藤正幸

    第39回情報理論とその応用シンポジウム(SITA2016) 

    Presentation date: 2016.12

  • ECサイトにおける購買行動データの学習に基づくクーポン効果最大化モデル

    遠藤 海太郎, 山下 遙, 後藤 正幸

    第39回情報理論とその応用シンポジウム(SITA2016) 

    Presentation date: 2016.12

  • ECサイトにおける施策効果向上を目的としたマルコフ潜在クラスモデルに関する一考察

    松嵜祐樹, 三川健太, 後藤正幸

    第39回情報理論とその応用シンポジウム(SITA2016) 

    Presentation date: 2016.12

  • 同一カテゴリ内での二値判別を許容する符号表に基づくECOC多値分類法に関する一考察

    鈴木玲央奈, 山下 遥, 後藤正幸

    第39回情報理論とその応用シンポジウム(SITA2016) 

    Presentation date: 2016.12

  • 気象情報とTweetデータの統合分析に基づく体感気温の定量化について

    馬賀嵩士, 三川健太, 後藤正幸, 吉開朋弘

    日本経営工学会 平成28年度秋季大会 

    Presentation date: 2016.10

  • トピックモデルを用いた未観測カテゴリを含む文書集合の自動分類について

    山本祐生, 三川健太, 後藤正幸

    日本経営工学会 平成28年度秋季大会 

    Presentation date: 2016.10

  • 混合回帰モデルに基づく就職ポータルサイトの被エントリ数予測に関する一考察

    永森誠矢, 山下 遙, 後藤正幸, 荻原大陸

    日本経営工学会 平成28年度秋季大会 

    Presentation date: 2016.10

  • 施策効果の最適化を目的としたマルコフ潜在クラスモデルによる購買行動分析

    松嵜祐樹, 三川健太, 後藤正幸

    日本経営工学会 平成28年度秋季大会 

    Presentation date: 2016.10

  • 同一カテゴリ内での二値判別を許容する符号表に基づくECOC多値文書分類法

    鈴木 玲央奈, 山下 遙, 後藤正幸

    日本経営工学会 平成28年度秋季大会 

    Presentation date: 2016.10

  • 購買履歴データを用いた顧客の嗜好の抽出に関する一考察

    伊藤寛彬, 山下 遙, 後藤正幸

    日本経営工学会 平成28年度秋季大会 

    Presentation date: 2016.10

  • 気象条件を考慮した商品売上パターン分析に関する一考察

    岡山 成, 山下 遙, 後藤正幸, 吉開朋弘

    日本経営工学会 平成28年度秋季大会 

    Presentation date: 2016.10

  • ECサイトにおけるクーポン効果最大化モデルの構築に関する一考察

    遠藤 海太郎, 山下 遙, 後藤 正幸

    日本経営工学会 平成28年度秋季大会 

    Presentation date: 2016.10

  • Tweetデータを対象としたWord2Vecによる商品分析に関する一考察

    後藤正幸, 三川健太, 山下 遙, 吉開朋弘

    日本経営工学会 平成28年度秋季大会 

    Presentation date: 2016.10

  • ECOC法による多値文書分類における符号語構成における一考察

    雲居玄道, 小林 学, 後藤正幸, 平澤茂一

    第15回情報科学技術フォーラム(FIT2016) 

    Presentation date: 2016.09

  • サブカテゴリを用いたECOC法による多値文書分類に関する一考察

    鈴木玲央奈, 山下 遥, 後藤正幸

    第15回情報科学技術フォーラム(FIT2016) 

    Presentation date: 2016.09

  • 気象情報とTweetデータの統合的分析による体感気温の定量化に関する一考察

    馬賀嵩士, 三川健太, 後藤正幸, 吉開朋弘

    第15回情報科学技術フォーラム(FIT2016) 

    Presentation date: 2016.09

  • トピックモデルに基づく協調フィルタリングによる文書推薦手法について

    山本祐生, 三川健太, 後藤正幸

    第15回情報科学技術フォーラム(FIT2016) 

    Presentation date: 2016.09

  • 企業の採用活動と被エントリ数の関係性に着目した分析モデルに関する一考察

    永森誠矢, 山下 遥, 後藤正幸, 荻原大陸

    第15回情報科学技術フォーラム(FIT2016) 

    Presentation date: 2016.09

  • 顧客セグメンテーションを目的とした潜在クラスモデルによる購買行動分析に関する一考察

    松嵜祐樹, 三川健太, 後藤正幸

    第15回情報科学技術フォーラム(FIT2016) 

    Presentation date: 2016.09

  • 潜在クラスモデルに基づく初期購買を考慮したRFM分析モデルに関する一考察

    張 倩, 山下 遥, 三川健太, 後藤正幸

    第15回情報科学技術フォーラム(FIT2016) 

    Presentation date: 2016.09

  • 説明変数に対する属性別パラメータを考慮した判別モデル

    山下 遥, 後藤正幸

    第15回情報科学技術フォーラム(FIT2016) 

    Presentation date: 2016.09

  • ECサイトにおける閲覧端末を考慮したクーポン効果最大化モデルの構築

    遠藤海太郎, 山下 遥, 後藤正幸

    第15回情報科学技術フォーラム(FIT2016) 

    Presentation date: 2016.09

  • Learning Analyticsのための学習履歴可視化システムの開発

    佐藤一裕, 荒本道隆, 中澤 真, 小林 学, 中野美知子, 後藤正幸, 平澤茂一

    経営情報学会2016年秋季全国研究発表大会 

    Presentation date: 2016.09

  • 2値判別器を用いた多値分類方式のシステム評価

    平澤茂一, 雲居玄道, 小林 学, 後藤正幸, 稲積宏誠

    経営情報学会2016年秋季全国研究発表大会 

    Presentation date: 2016.09

  • アンサンブル学習に基づく近似ベイズ予測アルゴリズムの提案

    荒井琢充, 山本祐生, 三川健太, 後藤正幸

    日本経営工学会 平成28年度春季大会 

    Presentation date: 2016.05

  • 区役所窓口を対象とした来庁者滞在時間予測モデルの構築

    数藤 光太郎, 阿内宏武, 雲居玄道, 後藤正幸

    日本経営工学会 平成28年度春季大会 

    Presentation date: 2016.05

  • 局所的距離学習と適応的重み付け和に基づくk最近傍法の分類精度向上に関する一考察

    中野修平, 永森誠矢, 三川健太, 後藤正幸

    日本経営工学会 平成28年度春季大会 

    Presentation date: 2016.05

  • 季節性商品への嗜好を考慮した顧客クラスタリング手法に関する提案

    仁ノ平 将人, 張 倩, 鈴木玲央奈, 山下 遥, 後藤正幸

    日本経営工学会 平成28年度春季大会 

    Presentation date: 2016.05

  • 就職ポータルサイトにおける企業のアピールポイントと学生の志望理由のマッチング分析に関する一考察

    坂元哲平, 鈴木玲央奈, 山下 遥, 後藤正幸, 荻原大陸

    日本経営工学会 平成28年度春季大会 

    Presentation date: 2016.05

  • サポートベクトルに着目したECOC-SVMによる多値分類に関する一考察

    山極綾子, 馬賀嵩士, 山下 遥, 後藤正幸

    日本経営工学会 平成28年度春季大会 

    Presentation date: 2016.05

  • 編集履歴可視化システムを用いたLearning Analytics 〜Cプログラミング科目における編集履歴と評価得点データを統合した分析モデル

    後藤正幸, 三川健太, 雲居玄道, 小林 学, 荒本道隆, 平澤茂一

    情報処理学会 第78回全国大会, 5F-05 

    Presentation date: 2016.03

  • 編集履歴可視化システムを用いたLearning Analytics 〜Scratchを用いた初等教育向けプログラミング教育における学習者の思考パターン分析

    中澤 真, 荒本道隆, 後藤正幸, 平澤茂一

    情報処理学会 第78回全国大会, 5F-04 

    Presentation date: 2016.03

  • 編集履歴可視化システムを用いたLearning Analytics 〜システム構成と実装

    荒本道隆, 小林 学, 中澤 真, 中野美知子, 後藤正幸, 平澤茂一

    情報処理学会 第78回全国大会, 5F-02 

    Presentation date: 2016.03

  • データの転送制御に基づく効率的な分散型SVMの学習法

    湯川 輝一朗, 三川健太, 後藤正幸

    電子情報通信学会 技術研究報告 人工知能と知識処理研究会(AI), Vol.115, No.381, AI2015-51 

    Presentation date: 2015.12

  • 複数の局所的距離の学習法とその統合による分類手法に関する一考察

    齋藤 洋, 三川健太, 後藤正幸

    電子情報通信学会 技術研究報告 人工知能と知識処理研究会(AI), Vol.115, No.381, AI2015-50 

    Presentation date: 2015.12

  • ブートストラップ法を用いたAlternating Decision Forestsの適応的な汎化性能向上法

    三沢翔太郎, 三川健太, 後藤正幸

    電子情報通信学会 技術研究報告 人工知能と知識処理研究会(AI), Vol.115, No.381, AI2015-49 

    Presentation date: 2015.12

  • 就職ポータルサイトにおける被エントリ数の予測モデルに関する一考察

    野津琢登, 三川健太, 後藤正幸, 荻原大陸

    電子情報通信学会 技術研究報告 人工知能と知識処理研究会(AI), Vol.115, No.381, AI2015-34 

    Presentation date: 2015.12

  • モデルクラスを拡張した場合のベイズ予測アルゴリズムに関する一考察

    阿内宏武, 三川健太, 後藤正幸

    第38回情報理論とその応用シンポジウム(SITA2015) 

    Presentation date: 2015.11

  • 潜在クラスモデルに基づく学生の就職活動終了日予測モデルに関する一考察

    山上 敢, 三川健太, 後藤正幸, 荻原大陸

    第38回情報理論とその応用シンポジウム(SITA2015) 

    Presentation date: 2015.11

  • ECOC多値判別手法に対するコスト考慮型学習に関する一考察

    安田直生, 三川健太, 後藤正幸

    第38回情報理論とその応用シンポジウム(SITA2015) 

    Presentation date: 2015.11

  • ベータ分布を導入したpLSAモデルに基づく協調フィルタリング

    楊 添翔, 板垣直矢, 三川健太, 後藤正幸

    日本経営工学会 平成27年度秋季大会 

    Presentation date: 2015.11

  • 就職ポータルサイトにおける各企業の被エントリ数の予測モデルに関する一考察

    野津琢登, 三川健太, 後藤正幸, 荻原大陸

    日本経営工学会 平成27年度秋季大会 

    Presentation date: 2015.11

  • 3-modeデータにおける行列分解を考慮したクラスタリング手法

    山下 遥, 後藤正幸

    日本経営工学会 平成27年度秋季大会 

    Presentation date: 2015.11

  • ビジュアルプログラミング言語「Scratch」のための学習履歴分析環境とその可能性−初等教育からのプログラミング教育に向けて−

    中澤 真, 後藤正幸, 荒本道隆, 平澤茂一

    日本経営工学会 平成27年度秋季大会 

    Presentation date: 2015.11

  • プログラミング編集履歴可視化システムとその実践

    小林 学, 後藤正幸, 荒本道隆, 平澤茂一

    日本経営工学会 平成27年度秋季大会 

    Presentation date: 2015.11

  • 多値判別問題におけるコスト考慮型学習への拡張に関する一考察

    安田直生, 三川健太, 後藤正幸

    日本経営工学会 平成27年度秋季大会 

    Presentation date: 2015.11

  • 局所的構造をモデル化可能な計量距離学習に関する一考察

    齋藤 洋, 三川健太, 後藤正幸

    日本経営工学会 平成27年度秋季大会 

    Presentation date: 2015.11

  • 計算量の削減を目的とした分散型SVMの学習手法に関する一考察

    湯川輝一朗, 三川健太, 後藤正幸

    日本経営工学会 平成27年度秋季大会 

    Presentation date: 2015.11

  • 汎化性能を考慮したAlternating Decision Forestsの改良に関する一考察

    三沢翔太郎, 三川健太, 後藤正幸

    日本経営工学会 平成27年度秋季大会 

    Presentation date: 2015.11

  • 生起順序と累積生起回数が混在する条件付き確率モデルに対するベイズ予測アルゴリズム

    阿内宏武, 三川健太, 後藤正幸

    日本経営工学会 平成27年度秋季大会 

    Presentation date: 2015.11

  • 学生の属性情報と行動履歴情報を用いた就職活動終了日予測モデルの構築

    山上 敢, 三川健太, 後藤正幸, 荻原大陸

    日本経営工学会 平成27年度秋季大会 

    Presentation date: 2015.11

  • 代表元間の距離構造を用いた計量距離学習に関する一考察

    三川健太, 小林 学, 後藤正幸, 平澤茂一

    日本経営工学会 平成27年度秋季大会 

    Presentation date: 2015.11

  • 離反顧客発見を目的とする判別分析手法に関する一考察

    酒井拓哉, 三川健太, 後藤正幸

    日本経営工学会 平成27年春季大会 

    Presentation date: 2015.05

  • 3元直交表に基づくECOC法による多値文書分類に関する一考察

    鈴木玲央奈, 山上 敢, 三川健太, 後藤正幸

    日本経営工学会 平成27年春季大会 

    Presentation date: 2015.05

  • 就職ポータルサイトにおける被エントリ数の予測に関する一考察

    野津琢登, 三川健太, 後藤正幸, 荻原大陸

    日本経営工学会 平成27年春季大会 

    Presentation date: 2015.05

  • 潜在トピックを考慮した未観測なカテゴリを含む文書集合の自動分類手法の提案

    山本祐生, 雲居玄道, 三川健太, 後藤正幸

    日本経営工学会 平成27年春季大会 

    Presentation date: 2015.05

  • 購買情報を用いたRFM分析に基づく顧客分析手法に関する一考察

    張 倩, 三川健太, 後藤正幸

    日本経営工学会 平成27年春季大会 

    Presentation date: 2015.05

  • エントリ傾向の時間的変化を考慮したユーザクラスタリングに基づく就職活動終了時期予測

    永森誠矢, 三川健太, 後藤正幸, 荻原大陸

    日本経営工学会 平成27年春季大会 

    Presentation date: 2015.05

  • クーポン付購買履歴データを用いた顧客購買行動分析に関する一考察

    松嵜祐樹, 山上 敢, 三川健太, 後藤正幸

    日本経営工学会 平成27年春季大会 

    Presentation date: 2015.05

  • Information-Theoretic Metric Learning の分類精度向上を目的とした学習データペアの選択法

    馬賀嵩士, 湯川 輝一朗, 三川健太, 後藤正幸

    日本経営工学会 平成27年春季大会 

    Presentation date: 2015.05

  • 出欠情報による隠れ属性モデル解析

    小林 学, 後藤正幸, 平澤茂一

    経営情報学会 2015年春季全国研究発表大会 

    Presentation date: 2015.05

  • 詳細な学習ログを用いた英語リーディング過程の分析 〜(3)リーディング過程における学習者モデル〜

    中澤 真, 梅澤克之, 小林 学, 小泉大城, 後藤正幸, 平澤茂一

    情報処理学会 第77回全国大会 

    Presentation date: 2015.03

  • ビジネスアナリティクス手法の大学教育への適用可能性

    後藤 正幸  [Invited]

    情報処理学会 第77回全国大会イベント企画, 「次世代eラーニング研究」シンポジウム(2)〜新たなICT活用による学びの変革〜 

    Presentation date: 2015.03

  • Learning Analyticsにおける学習履歴の情報構造と粒度のあり方

    中澤 真, 後藤正幸, 平澤茂一

    日本e-Learning学会 第17回学術講演会 

    Presentation date: 2015.02

  • 就職ポータルサイトにおける潜在クラスを用いたレコメンデーションモデルに関する一考察

    古山 亮, 三川健太, 後藤正幸

    第37回情報理論とその応用シンポジウム(SITA2014) 

    Presentation date: 2014.12

  • 閲覧及び購買行動を同時に表現する潜在クラスモデルの提案とその学習法

    藤原直広, 三川健太, 後藤正幸

    第37回情報理論とその応用シンポジウム(SITA2014) 

    Presentation date: 2014.12

  • 類似性に基づくラベル選択法を用いたマルチトピック文書分類

    秋山龍太郎, 雲居玄道, 三川健太, 後藤正幸

    第37回情報理論とその応用シンポジウム(SITA2014) 

    Presentation date: 2014.12

  • Large Margin Nearest Neighbor の分類精度向上を目的とした学習データの重み付けに関する一考察

    山崎史博, 三川健太, 後藤正幸

    第37回情報理論とその応用シンポジウム(SITA2014) 

    Presentation date: 2014.12

  • シンボルの累積出現回数を条件とするモデルクラスのベイズ予測アルゴリズムについて

    阿内宏武, 三川健太, 雲居玄道, 後藤正幸

    第37回情報理論とその応用シンポジウム(SITA2014) 

    Presentation date: 2014.12

  • 就職ポータルサイトにおける潜在クラスを用いたレコメンドシステムに関する研究

    古山 亮, 三川健太, 後藤正幸, 谷田部 治明

    日本経営工学会 平成26年秋季大会 

    Presentation date: 2014.11

  • カテゴリの統計的特徴を利用した適応的計量距離学習に関する一考察

    三川健太, 後藤正幸

    日本経営工学会 平成26年秋季大会 

    Presentation date: 2014.11

  • 混合クラスモデルの混合比に着目したクラスタリング手法の提案

    山上 敢, 三川健太, 後藤正幸, 谷田部 治明

    日本経営工学会 平成26年秋季大会 

    Presentation date: 2014.11

  • ECOC SVMにおけるデータ数の偏りを考慮した多値文書分類手法に関する一考察

    安田直生, 雲居玄道, 三川健太, 後藤正幸

    日本経営工学会 平成26年秋季大会 

    Presentation date: 2014.11

  • シンボルの出現回数を考慮したベイズ予測アルゴリズムに関する一考察

    阿内宏武, 三川健太, 雲居玄道, 後藤正幸

    日本経営工学会 平成26年秋季大会 

    Presentation date: 2014.11

  • ラベルごとのデータ数のバランスを考慮したマルチトピック文書分類

    秋山龍太郎, 雲居玄道, 後藤正幸

    日本経営工学会 平成26年秋季大会 

    Presentation date: 2014.11

  • 潜在クラスモデルに基づくユーザ行動履歴データの分析

    後藤正幸

    日本経営工学会 平成26年秋季大会 

    Presentation date: 2014.11

  • Large Margin Nearest Neighbor の分類精度向上を目的とした学習データの重み付けに関する一考察

    山崎史博, 後藤正幸, 三川健太

    計測自動制御学会 システム・情報部門学術講演会,SSI2014,SS27-11 

    Presentation date: 2014.11

  • 閲覧及び購買行動を同時に学習可能な潜在クラスモデルの提案

    藤原直広, 三川健太, 後藤正幸

    計測自動制御学会 システム・情報部門学術講演会,SSI2014,SS27-10 

    Presentation date: 2014.11

  • 学習データの被予測性能に着目したAlternating Decision Forestsの各決定木への重み付け予測法

    三沢翔太郎, 藤原直広, 三川健太, 後藤正幸

    電子情報通信学会 技術研究報告 情報理論(IT),Vol.114, No.138 

    Presentation date: 2014.07

  • 低次元計量行列の学習とその結合による計量行列学習の計算量削減法

    齊藤 洋, 山崎史博, 三川健太, 後藤正幸

    電子情報通信学会 技術研究報告 情報理論(IT),Vol.114, No.138 

    Presentation date: 2014.07

  • 出席状況把握システムSAMSとその解析

    小林学, 大谷真, 梅澤克之, 後藤正幸, 平澤茂一

    経営情報学会 2014年春季全国研究発表大会 

    Presentation date: 2014.05

  • 詳細な学習履歴を活用した学習者行動の分析

    中澤真, 小泉大城, 後藤正幸, 平澤茂一

    情報処理学会第76回全国大会 

    Presentation date: 2014.03

  • ネパールのチトワン地域における観光ビジネスに関するフィールド分析

    宇田川 宙, マニタ・シュレスタ, 後藤正幸, ブレンダ・ブッシェル

    日本経営工学会 平成25年秋季大会 

    Presentation date: 2013.11

  • l1正則化を用いた計量距離学習による特徴選択に関する一考察

    三川健太, 石田 崇, 後藤正幸, 平澤茂一

    日本経営工学会 平成25年秋季大会 

    Presentation date: 2013.11

  • ユーザレビューの構造を利用したマトリックス分析の提案

    李 昇炯, 大森悠矢, 三川健太, 後藤正幸

    日本経営工学会 平成25年秋季大会 

    Presentation date: 2013.11

  • e-learningにおける学習スタイル−協働学習と学習ログ解析

    中澤 真, 小泉大城, 石田 崇, 後藤正幸, 平澤茂一

    日本経営工学会 平成25年秋季大会 

    Presentation date: 2013.11

  • e-learningにおける学習スタイル−ネットワーク品質をオンデマンド授業

    平澤茂一, 中澤 真, 小泉大城, 石田 崇, 後藤正幸

    日本経営工学会 平成25年秋季大会 

    Presentation date: 2013.11

  • ランダムな次元削減とアンサンブルによるメトリックラーニングの計算量低減法

    斎藤 洋, 山崎史博, 三川健太, 後藤正幸

    日本経営工学会 平成25年秋季大会 

    Presentation date: 2013.11

  • 潜在クラスモデルを用いた学生の就職活動エントリー予測に関する一考察

    峯苫和史, 三川健太, 石田 崇, 後藤正幸, 小川晋一郎

    第36回情報理論とその応用シンポジウム(SITA2013) 

    Presentation date: 2013.11

  • スパースなデータを対象とした潜在クラスモデルに基づく協調フィルタリングに関する一考察

    坂本俊輔, 三川健太, 後藤正幸

    第36回情報理論とその応用シンポジウム(SITA2013) 

    Presentation date: 2013.11

  • 評価と購買の両履歴データを用いる潜在クラスモデルの推定アルゴリズムに関する一考察

    大井貴裕, 三川健太, 石田 崇, 後藤正幸

    第36回情報理論とその応用シンポジウム(SITA2013) 

    Presentation date: 2013.11

  • 高次元かつスパースなベクトル空間におけるl1正則化に基づく計量距離学習に関する一考察

    三川健太, 石田 崇, 小林 学, 後藤正幸, 平澤茂一

    第36回情報理論とその応用シンポジウム(SITA2013) 

    Presentation date: 2013.11

  • 就職ポータルサイトにおけるレコメンデーションモデルに関する一考察

    大森悠矢, 三川健太, 石田 崇, 後藤正幸, 小川晋一郎

    第36回情報理論とその応用シンポジウム(SITA2013) 

    Presentation date: 2013.11

  • 層別木と混合ワイブル分布に基づく就職活動終了時期の予測モデル

    早川真央, 三川健太, 石田 崇, 後藤正幸, 小川晋一郎

    第36回情報理論とその応用シンポジウム(SITA2013) 

    Presentation date: 2013.11

  • 単語自動取得手法を用いた擬似単語N-gramによる文書分類

    鈴木 誠, 山岸直秀, 蔡 宜静, 後藤正幸

    第36回情報理論とその応用シンポジウム(SITA2013) 

    Presentation date: 2013.11

  • Reed Muller符号を用いた階層的ECOC法による多値文書分類

    荻原大陸, 三川健太, 後藤正幸

    第36回情報理論とその応用シンポジウム(SITA2013) 

    Presentation date: 2013.11

  • e-learning における学習スタイルに関する一考察

    平澤 茂一, 後藤 正幸, 中澤 真, 石田 崇, 小泉 大城

    経営情報学会 2013年秋季全国研究発表大会 

    Presentation date: 2013.10

    Event date:
    2013.10
     
     
  • Large Margin Nearest Neighborの分類精度向上を目的とした学習データ選択法に関する一考察

    山崎史博, 峯苫和史, 三川健太, 後藤正幸

    日本経営工学会 平成25年春季大会 

    Presentation date: 2013.05

  • 就職ポータルサイトにおける企業紹介文評価のための有用単語評価法

    古山 亮, 雲居玄道, 後藤正幸

    日本経営工学会 平成25年春季大会 

    Presentation date: 2013.05

  • 三者以上でプライバシーを保護する線形回帰モデルの分散計算法について

    湯川輝一朗, 荻原大陸, 三川健太, 後藤正幸

    日本経営工学会 平成25年春季大会 

    Presentation date: 2013.05

  • 線形回帰モデルの混合を用いた層別木モデルによるベイズ最適な予測法

    藤原直広, 早川真央, 石田 崇, 後藤正幸

    日本経営工学会 平成25年春季大会 

    Presentation date: 2013.05

  • 判別を目的としたプライバシー保護データ解析に関する一考察

    後藤正幸, 須子統太, 小林 学, 平澤茂一

    日本経営工学会 平成25年春季大会 

    Presentation date: 2013.05

  • エージェントベースシミュレーションによるモデルベース協調フィルタリングの評価に関する一考察

    井沢祐介, 三川健太, 後藤正幸

    第35回情報理論とその応用シンポジウム(SITA2012) 

    Presentation date: 2012.12

  • 確率モデルに基づくAggregate Diversityを考慮した推薦システムに関する一考察

    鈴木健史, 三川健太, 後藤正幸

    第35回情報理論とその応用シンポジウム(SITA2012) 

    Presentation date: 2012.12

  • プライバシー保護を目的とした線形回帰モデルにおける最小二乗推定量の分散計算法について

    須子統太, 堀井俊佑, 小林 学, 後藤正幸, 松嶋敏泰, 平澤茂一

    第15回情報論的学習理論ワークショップ(IBIS2012) 

    Presentation date: 2012.11

  • テキスト分類問題におけるカテゴリ情報を用いた適応的距離学習に関する一考察

    三川健太, 石田 崇, 後藤正幸, 平澤茂一

    第15回情報論的学習理論ワークショップ(IBIS2012) 

    Presentation date: 2012.11

  • フォークソノミーを利用した単語間の関連によるWebページ検索システム

    早川真央, 三川健太, 後藤正幸, 大和田 勇人

    日本経営工学会 平成24年秋季大会 

    Presentation date: 2012.11

  • 文書分類問題におけるカテゴリ情報を用いた適応的重み学習に関する一考察

    三川健太, 石田 崇, 後藤正幸

    日本経営工学会 平成24年秋季大会 

    Presentation date: 2012.11

  • 符号理論に基づくECOC法による多値パターン分類に関する一考察

    荻原大陸, 三川健太, 後藤正幸

    日本経営工学会 平成24年秋季大会 

    Presentation date: 2012.11

  • ユーザの評価傾向と潜在クラスを考慮した推薦手法に関する一考察

    大森悠矢, 三川健太, 後藤正幸

    日本経営工学会 平成24年秋季大会 

    Presentation date: 2012.11

  • 目的変数がポアソン分布に従う決定木モデルにおけるベイズ最適なアルゴリズム

    峯苫和史, 石田 崇, 後藤正幸

    日本経営工学会 平成24年秋季大会 

    Presentation date: 2012.11

  • ECOC法における分類器の予測精度を考慮した分類法に関する一考察

    石橋想太郎, 三川健太, 石田 崇, 後藤正幸

    電子情報通信学会 技術研究報告 人工知能と知識処理研究会(AI),IEICE-112,AI-319 

    Presentation date: 2012.11

  • 特徴語に注目したSmith-Watermanアルゴリズムに基づく剽窃ソースコードの自動検出手法

    日比健太, 雲居玄道, 三川健太, 後藤正幸

    電子情報通信学会 技術研究報告 人工知能と知識処理研究会(AI),IEICE-112,AI-319 

    Presentation date: 2012.11

  • 地域の経済・社会・環境に適応した理想的な地域社会を議論する環境教育プログラムの実践 〜ネパールの公立中学生を対象として〜

    原田雪穂, マニタ・シュレスタ, 後藤正幸, ブレンダ・ブッシェル

    日本環境教育学会 第23回大会(東京) 

    Presentation date: 2012.08

  • クラスタリング手法を導入したImproved Naive Bayes法による推薦システム

    大井貴裕, 荒川貴紀, 三川健太, 後藤正幸

    日本経営工学会 平成24年春季大会 

    Presentation date: 2012.05

  • 混合制約付き潜在ディリクレ配分法に基づく推薦システムに関する一考察

    坂本俊輔, 井沢祐介, 三川健太, 後藤正幸

    日本経営工学会 平成24年春季大会 

    Presentation date: 2012.05

  • 文書分類問題におけるカテゴリに注目した可変長Nグラム法

    井上大樹, 雲居玄道, 三川健太, 後藤正幸

    日本経営工学会 平成24年春季大会 

    Presentation date: 2012.05

  • フルオンデマンド授業における学生アンケートの分析

    石田 崇, 畑上英毅, 後藤正幸, 平澤茂一

    情報処理学会第74 回全国大会 

    Presentation date: 2012.03

  • 選択型と記述型の学生アンケートの分析

    平澤茂一, 石田 崇, 雲居玄道, 後藤正幸

    情報処理学会第74 回全国大会 

    Presentation date: 2012.03

  • マルコフモデルによる自動分類に対する分類誤り確率の推定

    小林 学, 後藤正幸, 松嶋敏泰, 平澤茂一

    情報処理学会第74 回全国大会 

    Presentation date: 2012.03

  • 文脈木重みづけ法を用いた文書分類の誤り確率について

    小林学, 後藤正幸, 松嶋敏泰, 平澤茂一

    電子情報通信 学会技術研究報告NLP, NLP2011-111 

    Presentation date: 2011.11

  • 混合決定木モデルによる連続変数の予測法に関する一考察

    坂口卓也, 石田 崇, 後藤正幸

    第10回情報科学技術フォーラム FIT2011 

    Presentation date: 2011.09

  • 商品の比較履歴とユーザーレビューに基づく推薦手法に関する一考察

    榮枝隼人, 三川健太, 後藤正幸

    第10回情報科学技術フォーラム FIT2011 

    Presentation date: 2011.09

  • 二値判別器の組み合わせによるRVM多値文書分類手法に関する一考察

    小田井良輔, 雲居玄道, 三川健太, 後藤正幸

    第10回情報科学技術フォーラム FIT2011 

    Presentation date: 2011.09

  • アルファベットサイズが未知の情報源に対する効率的なベイズ符号化法の一考察

    岩間大輝, 石田 崇, 後藤正幸

    第10回情報科学技術フォーラム FIT2011 

    Presentation date: 2011.09

  • 二元対称消失通信路におけるビット反転復号法の改善

    石橋想太郎, 長田佳史, 細谷 剛, 後藤正幸

    第10回情報科学技術フォーラム FIT2011 

    Presentation date: 2011.09

  • 検査行列の構造を利用したLDPC符号のパンクチャ法に関する一考察

    長田佳史, 細谷 剛, 後藤正幸

    第10回情報科学技術フォーラム FIT2011 

    Presentation date: 2011.09

  • ゴミ問題に対する意識を向上させる市民参加型プログラム〜ネパール・カトマンズにおけるスポーツゴミ拾い〜

    佐々木麻衣, 岡田啓, リジャルH.B, 後藤正幸, ブレンダ・ブッシェル

    日本環境教育学会 第22回大会(青森) 

    Presentation date: 2011.07

  • 問題の構造を考える環境教育プログラムの実践― ネパールの小・中学生を対象として ―

    宇敷裕子, シュレスタ・マニタ, 後藤正幸, リジャルH.B, 岡田啓, ブレンダ・ブッシェル

    日本環境教育学会 第22回大会(青森) 

    Presentation date: 2011.07

  • ネパールの内発的発展を目指す参加型討論―ステークホルダーネットワーキングの形成として―

    シュレスタ・マニタ, 鈴木祐志郎, 後藤正幸, 松葉口玲子, ブレンダ・ブッシェル

    日本環境教育学会 第22回大会(青森) 

    Presentation date: 2011.07

  • 拡張余弦尺度を用いた距離学習に関する一考察

    三川健太, 石田 崇, 後藤正幸

    日本経営工学会 平成23年春季大会 

    Presentation date: 2011.05

  • 複数要素から構成される文書データの分類手法に関する一考察 〜階層潜在クラスモデルと階層EMアルゴリズムの提案〜

    荒川貴紀, 三川健太, 後藤正幸

    日本経営工学会 平成23年春季大会 

    Presentation date: 2011.05

  • アイテム評価値の高低を考慮した混合メンバーシップ・ブロックモデルによる推薦システム

    井沢祐介, 榮枝隼人, 三川健太, 後藤正幸

    日本経営工学会 平成23年春季大会 

    Presentation date: 2011.05

  • コーディングスタイルモデルに基づく剽窃ソースコードの自動検出手法

    日比健太, 雲居玄道, 三川健太, 後藤正幸

    日本経営工学会 平成23年春季大会 

    Presentation date: 2011.05

  • 確率潜在空間モデルに基づく推薦システムに関する研究

    鈴木健史, 雲居玄道, 三川健太, 後藤正幸

    日本経営工学会 平成23年春季大会 

    Presentation date: 2011.05

  • 高次元ベクトル空間における重み付け法について

    後藤正幸, 鈴木 誠, 平澤茂一

    日本経営工学会 平成23年春季大会 

    Presentation date: 2011.05

  • 一般化LDPC符号に対する効率的な符号化法

    寺本賢一, 細谷 剛, 後藤正幸, 平澤茂一

    電子情報通信学会, 技術研究報告IT2010-67 

    Presentation date: 2011.01

  • 2元系列のメッセージを用いたビット反転復号法の改良

    谷口祐樹, 細谷 剛, 後藤正幸, 平澤茂一

    電子情報通信学会, 技術研究報告IT2010-64 

    Presentation date: 2011.01

  • 連続変数に対応した決定木モデルにおけるベイズ最適な予測アルゴリズム

    坂口卓也, 寺本賢一, 石田 崇, 後藤正幸

    経営情報学会 秋季全国研究発表大会 

    Presentation date: 2010.11

  • 最大被覆問題に基づくユーザレビュー集約手法に関する一考察

    竹村隆, 雲居玄道, 後藤正幸

    経営情報学会 秋季全国研究発表大会 

    Presentation date: 2010.11

  • 事後確率最大判別法に基づくRVM多値文書分類手法の提案

    小田井良輔, 谷口祐樹, 雲居玄道, 後藤正幸

    経営情報学会 秋季全国研究発表大会 

    Presentation date: 2010.11

  • 学生アンケートに基づくe-learning 授業評価モデルの検討

    石田 崇, 雲居玄道, 後藤正幸, 後藤幸功, 平澤茂一

    経営情報学会 秋季全国研究発表大会 

    Presentation date: 2010.11

  • ソーシャルブックマークにおけるユーザのタグ付け傾向を加味したWebページ推薦手法

    岸端祐季, 石田 崇, 後藤正幸

    電子情報通信学会, 技術研究報告 AI2010-39 

    Presentation date: 2010.11

  • 評価関数の重みパラメータを推定する対話型遺伝的アルゴリズム

    石川英太郎, 石田 崇, 後藤正幸

    電子情報通信学会, 技術研究報告 AI2010-37 

    Presentation date: 2010.11

  • PLSIを用いた文書分類手法に関する一考察

    雲居玄道, 石田 崇, 後藤正幸, 平澤茂一

    電子情報通信学会, 技術研究報告 AI2010-33 

    Presentation date: 2010.11

  • 異なる拡張率をもつ島モデルによる実数値GAのSPXによる構成法

    大串康輝, 石川英太郎, 石田 崇, 後藤正幸

    日本経営工学会平成22年度秋季研究大会 

    Presentation date: 2010.10

  • 宿泊施設を対象とした評価サイトにおけるユーザーレビュー分析に関する一考察

    榮枝隼人, 三川健太, 後藤正幸

    日本経営工学会平成22年度秋季研究大会 

    Presentation date: 2010.10

  • ビュッフェ形式レストランにおける食材残渣削減のための材料発注方法の研究

    原田繁幸, 増井忠幸, 後藤正幸, 山田哲男

    日本経営工学会平成22年度秋季研究大会 

    Presentation date: 2010.10

  • 国産木材利用促進に関する一研究

    大岡 徹, 増井忠幸, 山田哲男, 後藤正幸

    日本経営工学会平成22年度秋季研究大会 

    Presentation date: 2010.10

  • 満足度を考慮したユーザレビューの分析手法に関する一考察

    三川健太, 石田 崇, 後藤正幸

    日本経営工学会平成22年度秋季研究大会 

    Presentation date: 2010.10

  • 高次元空間の確率モデルにおける統計的性質について

    後藤正幸

    日本経営工学会平成22年度秋季研究大会 

    Presentation date: 2010.10

  • 高符号化率までパンクチャ可能なLDPC符号に関する一考察

    長田佳史, 寺本賢一, 細谷 剛, 後藤正幸

    電子情報通信学会, 技術研究報告 IT2010-28, Vol.110, No.137 

    Presentation date: 2010.07

  • 混合Polya分布に基づくサブカテゴリを考慮した文書分類手法

    牛尼夏海, 雲居玄道, 石田 崇, 後藤正幸

    電子情報通信学会, 技術研究報告 IT2010-13,Vol.110, No.137 

    Presentation date: 2010.07

  • アルファベットが未知の場合の木情報源に対する効率的ベイズ符号化アルゴリズム

    岩間大輝, 寺本賢一, 石田 崇, 後藤正幸

    電子情報通信学会, 技術研究報告 IT2010-11,Vol.110, No.137 

    Presentation date: 2010.07

  • 協調フィルタリングに基づく情報推薦手法の漸近評価

    後藤正幸, 鈴木 誠, 石田 崇, 平澤茂一

    日本経営工学会 平成22年度春季研究大会 

    Presentation date: 2010.05

  • 学生視点に基づくWBT授業コンテンツの評価に関する一考察

    後藤 正幸, 石田 崇, 川原 洋, 平澤 茂一

    経営情報学会 2009年秋季全国研究発表大会 

    Presentation date: 2009.11

  • 観光地の指定とスケジュールの多様性を考慮した観光スケジュール作成支援

    石川英太郎, 石田 崇, 後藤正幸, 東 基衞

    第8回情報科学技術フォーラム講演論文集 FIT2009 

    Presentation date: 2009.09

  • フォークソノミーにおけるタグの意味的関係分析に関する一考察

    岸端佑季, 雲居玄道, 後藤正幸, 東 基衞

    第8回情報科学技術フォーラム講演論文集 FIT2009 

    Presentation date: 2009.09

  • ネパールフィールド研修プログラムを題材とした環境教育モジュールの開発と援用

    後藤 正幸, ブレンダ・ブッシェル, 岡田 啓

    CIEC 2009PCカンファレンス 

    Presentation date: 2009.08

  • メッセージ伝播型復号法に効果的な非正則LDPC符号の構成法と復号順序の決定法

    谷口 祐樹, 細谷 剛, 後藤 正幸, 平澤茂一

    電子情報通信学会, 技術研究報告, vol.109, no.143, IT2009-10 

    Presentation date: 2009.07

  • 一般化LDPC符号に対する部分符号の構造を利用した効率的な符号化法

    寺本 賢一, 細谷 剛, 後藤 正幸, 平澤茂一

    電子情報通信学会, 技術研究報告, vol.109, no.143, IT2009-10 

    Presentation date: 2009.07

  • レストランを対象とした店舗従業員満足度に関する研究

    林 秀貞, 増井忠幸, 後藤正幸

    日本経営工学会 春季大会 

    Presentation date: 2009.05

  • ビュッフェ形式レストランの顧客ベネフィット分析に関する一研究

    神鳥江里子, 増井忠幸, 後藤正幸

    日本経営工学会 春季大会 

    Presentation date: 2009.05

  • 著作権侵害文書検出のためのストリングカーネルを用いた要約文発見手法

    雲居玄道, 石田崇, 後藤正幸, 平澤茂一

    2008経営情報学会 秋季大会 

    Presentation date: 2008.11

  • Web上のユーザコメントを用いた価格プレミアム−顧客価値構造モデルの構築

    富田大介, 三川健太, 後藤正幸, 増井忠幸

    2008経営情報学会 秋季大会 

    Presentation date: 2008.11

  • ユーザレビューを用いた要求品質の構造分析に関する一考察

    井口琢斗, 富田大介, 田中慶二, 後藤正幸

    2008経営情報学会 秋季大会 

    Presentation date: 2008.11

  • 輸送過程における二酸化炭素排出量の詳細把握とその意義に関する一研究

    吉藤智一, 増井忠幸, 後藤正幸

    日本経営工学会 平成20年度秋季大会 

    Presentation date: 2008.10

  • 中古車の価格モデルとユーザベネフィット分析に関する一考察

    田中慶二, 富田大介, 後藤正幸, 渡部和雄

    日本経営工学会 平成20年度秋季大会 

    Presentation date: 2008.10

  • Webを用いたブランド・イメージ測定に関する研究

    中村 徹, 富田大介, 後藤正幸

    日本経営工学会 平成20年度秋季大会 

    Presentation date: 2008.10

  • ネパールにおける環境教育研修プログラムの評価に関する一考察

    清水恵子, シュレスタ・マニタ, 後藤正幸, 岡田 啓

    日本経営工学会 平成20年度秋季大会 

    Presentation date: 2008.10

  • ネパールを対象としたプロジェクト型環境教育モデルに関する一考察 〜 プロジェクトマネジメントの視点に基づく教育モデルの検討 〜

    瀬戸友貴, 今野夏希, 後藤正幸, 岡田 啓, ブレンダ・ブッシェル

    日本経営工学会 平成20年度秋季大会 

    Presentation date: 2008.10

  • ネパールを対象としたプロジェクト型環境教育モデルについて

    今野夏希, 瀬戸友貴, 後藤正幸, 岡田 啓, ブレンダ・ブッシェル

    日本環境教育学会第19回大会 

    Presentation date: 2008.08

  • 大学生を対象としたネパールにおける環境教育プログラム

    清水恵子, 阿部雄太, 後藤正幸, 岡田 啓, ブレンダ・ブッシェル

    日本環境教育学会第19回大会 

    Presentation date: 2008.08

  • 宅配サービスにおける物流効率向上のための顧客購入単価向上策に関する研究

    富田智恵, 鈴木美保, 後藤正幸, 増井忠幸

    日本経営工学会 平成20年度春季大会 

    Presentation date: 2008.05

  • 戦略事例の構造化による戦略アナロジー評価モデルの構築

    後藤 正幸, 原田繁幸, 田邊 亘

    経営情報学会 2007年秋季全国研究発表大会 

    Presentation date: 2007.11

  • テキスト分類問題を対象としたベクトル空間における距離構造の漸近解析に関する一考察

    後藤 正幸, 石田 崇, 鈴木 誠, 平澤茂一

    第30回情報理論とその応用シンポジウム 

    Presentation date: 2007.11

  • 価格プレミアム要因の構造分析に関する一考察

    富田大介, 林 翔希, 後藤 正幸

    日本経営工学会 平成19年度秋季大会 

    Presentation date: 2007.10

  • 自由記述文書データ分析における知識構造の援用に関する一考察

    渡辺 智幸, 三川 健太, 後藤 正幸

    日本経営工学会 平成19年度秋季大会 

    Presentation date: 2007.10

  • コンビニエンスストアにおける顧客の購買行動に関する研究

    浜 翔太郎, 後藤 正幸

    日本経営工学会 平成19年度秋季大会 

    Presentation date: 2007.10

  • スパースネスを考慮したベクトル空間モデルの統計的漸近解析について

    後藤 正幸

    日本経営工学会 平成19年度秋季大会 

    Presentation date: 2007.10

  • コンビニエンスストアにおける顧客の購買行動に関する研究

    浜 翔太郎, 上林 眞也, 後藤 正幸

    日本経営工学会 平成19年度春季大会 

    Presentation date: 2007.05

  • 知識構造を利用した文書データの自動分析に関する一考察

    渡辺 智幸, 三川 健太, 海老澤 卓哉, 後藤 正幸

    日本経営工学会 平成19年度春季大会 

    Presentation date: 2007.05

  • 文書分類モデルの性質に関する一考察

    後藤 正幸, 平澤 茂一

    第29回 情報理論とその応用シンポジウム 

    Presentation date: 2006.12

  • 統計モデルに基づく大学入試の理論的考察

    後藤 正幸, 石田 崇, 平澤 茂一

    経営情報学会 秋季全国研究発表大会 

    Presentation date: 2006.11

  • 顧客ロイヤリティ構造図に基づくユーザコメントの分析手法に関する一考察

    三川 健太, 後藤 正幸

    日本経営工学会 平成18年度秋季大会 

    Presentation date: 2006.11

  • 重要文抽出手法による自由記述アンケートの分析手法について

    渡辺 智幸, 後藤 正幸, 石田 崇, 平澤 茂一

    日本経営工学会 平成18年度秋季大会 

    Presentation date: 2006.11

  • 小学生を対象とした環境教育プログラムの評価法に関する一考察

    栗島 由美, 後藤 正幸, ブレンダ・ブッシェル

    日本経営工学会 平成18年度秋季大会 

    Presentation date: 2006.11

  • 大学生アンケートに基づく学生満足度の分析

    浜 翔太郎, 後藤 正幸

    日本経営工学会 平成18年度秋季大会 

    Presentation date: 2006.11

  • 文書分類モデルの統計的性質に関する一考察

    後藤 正幸, 平澤 茂一, 俵 信彦

    日本経営工学会 平成18年度秋季大会 

    Presentation date: 2006.11

  • 作業員の暗黙知に依存した作業工程の分析

    枝松 哲朗, 後藤 正幸, 薗部 祐希

    日本経営工学会 平成18年度秋季大会 

    Presentation date: 2006.11

  • ネパール-日本の連携による小学生を対象としたプログラムの設計と評価

    栗島 由美, 三川 健太, ブレンダ・ブッシェル, 後藤 正幸

    日本環境教育学会 第17回大会 

    Presentation date: 2006.08

  • 大学生を対象としたネパールプロジェクトの教育効果に関する一考察

    三川 健太, ブレンダ・ブッシェル, 栗島 由美, 後藤 正幸

    日本環境教育学会 第17回大会 

    Presentation date: 2006.08

  • 大学マーケティングのための市場分析に関する一考察

    浜 翔太郎, 三川 健太, 後藤 正幸

    日本経営工学会 春季大会 

    Presentation date: 2006.05

  • 消費者コミュニケーション手段としてのチラシ広告に関する一考察

    枝松 哲朗, 近藤 真史, 後藤 正幸, 渡邊 法比彦

    日本経営工学会 春季大会 

    Presentation date: 2006.05

  • ネパールと連携した環境教育プログラムの評価に関する一考察

    栗島 由美, 後藤 正幸, ブレンダ・ブッシェル

    日本経営工学会 春季大会 

    Presentation date: 2006.05

  • 継続購買につながる顧客ロイヤリティの構造分析手法に関する一考察

    三川 健太, 高橋 勉, 後藤 正幸

    日本経営工学会 春季大会 

    Presentation date: 2006.05

  • 情報検索技術を用いたアンケートデータの分析手法に関する研究

    渡辺 智幸, 後藤 正幸, 石田 崇, 平澤 茂一

    日本経営工学会 春季大会 

    Presentation date: 2006.05

  • 大学入試の理論

    後藤 正幸

    日本経営工学会 春季大会予稿集 

    Presentation date: 2006.05

  • オフィスビルにおける需要を対象とした文房具の共同配送に関する研究

    反田 晶久, 増井 忠幸, 後藤 正幸

    日本経営工学会 春季大会 

    Presentation date: 2006.05

  • 物流業務における二酸化炭素排出量算定のための情報伝達システムの提案

    河合伸幸, 増井忠幸, 後藤正幸

    経営情報学会,2005年秋季全国研究発表大会 

    Presentation date: 2005.11

  • 教学支援システムに関する学生アンケートの分析

    渡辺智幸, 後藤正幸, 石田嵩, 酒井哲也, 平澤茂一

    情報科学技術フォーラム FIT 2005 

    Presentation date: 2005.09

  • 制御理論に基づくミルクラン型物流システムの特性解析

    後藤正幸, 増井忠幸, 俵 信彦

    日本経営工学会 平成17年度秋季大会 

    Presentation date: 2005.09

  • ネパールにおける環境NGOのコミュニティマネジメントに関する一考察

    三川健太, ブレンダ・ブッシェル, 後藤正幸

    日本経営工学会 平成17年度秋季大会 

    Presentation date: 2005.09

  • シミュレーションによる共通EDIシステムの導入効果検証

    今 剛士, 後藤正幸, 増井忠幸

    日本経営工学会 平成17年度秋季大会 

    Presentation date: 2005.09

  • フェーズに合わせた中小企業の知財戦略

    枝松哲朗, 後藤正幸, 渡邊法比古, 薗部祐希

    日本経営工学会 平成17年度秋季大会 

    Presentation date: 2005.09

  • 日本とネパールの小学校ネットワーキングによる環境教育とその評価

    栗島由美, 後藤正幸, ブレンダ・ブッシェル

    日本経営工学会 平成17年度秋季大会 

    Presentation date: 2005.09

  • 大学生を対象としたオーストラリア熱帯雨林フィールドプログラムにおける情報教育とその評価

    後藤 正幸, 野村 迅史, 小堀 洋美

    日本環境教育学会 第16回大会研究発表要旨集 

    Presentation date: 2005.06

  • 物流システムのための情報チェーンに関する一考察

    後藤 正幸, 増井 忠幸

    経営情報学会 2005年春季全国研究発表大会 

    Presentation date: 2005.06

  • 文書分類技法とそのアンケート分析への応用

    平澤 茂一, 石田 崇, 足立 鉱史, 後藤 正幸, 酒井 哲也

    経営情報学会 2005年春季全国研究発表大会 

    Presentation date: 2005.06

  • インターネットを用いた研究支援環境

    石田 崇, 足立 鉱史, 後藤 正幸, 酒井 哲也, 平澤 茂一

    経営情報学会 2005年春季全国研究発表大会 

    Presentation date: 2005.06

  • ミルクラン方式のアナロジーによる直送と一括配送の混合配送方式について

    後藤 正幸

    日本経営工学会 平成17年度春季大会 

    Presentation date: 2005.05

  • 介護認定シミュレーションによる地域リハビリテーションの将来動向に関する考察

    後藤 正幸, 藤田 昌子, 太田 久彦, 小林 順子, 大久保 寛基, 大成 尚

    日本経営工学会 平成17年度春季大会 

    Presentation date: 2005.05

  • 実験計画法を用いたオリジナルプリントTシャツの品質最適化

    枝松 哲郎, 後藤 正幸, 渡邊 法比古, 園部 祐希

    日本経営工学会 平成17年度春季大会 

    Presentation date: 2005.05

  • 大学生を対象としたオーストラリア熱帯雨林フィールドプログラムの実践とその学生による評価

    小堀 洋美, Robyn Wilson, 野村 迅史, 日野 淳郎, 後藤 正幸

    日本環境教育学会 第16回大会研究発表要旨集 

    Presentation date: 2005.05

  • 多段階在庫の汎用性に関する情報理論的考察

    後藤正幸, 増井忠幸, 平澤茂一

    第27回情報理論とその応用シンポジウム 

    Presentation date: 2004.12

  • リハビリテーション診療支援データベースの開発―評価方法の妥当性検討―

    太田久彦, 小林順子, 木村哲彦, 高倉保幸, 後藤正幸, 大久保寛基, 大成 尚

    第24回医療情報学連合大会 第5回日本医療情報学会学術大会 

    Presentation date: 2004.11

  • UMLに基く物流情報モデルの構造分析手法に関する一考察

    後藤正幸, 増井忠幸

    経営情報学会 2004年度秋季全国研究発表大会 

    Presentation date: 2004.11

  • 情報検索技術を用いた選択式・自由記述式の学生アンケート解析

    石田 崇, 足立鉱史, 後藤正幸, 酒井哲也, 平澤茂一

    経営情報学会 2004年度秋季全国研究発表大会 

    Presentation date: 2004.11

  • サプライヤーと小売店の統合協力モデルに関する研究

    聶 炎, 増井忠幸, 後藤正幸

    日本経営工学会 平成16年度秋季大会 

    Presentation date: 2004.10

  • Building Learner Autonomy through Web Based Training in Media English

    Kuniko Yoshida, Shintaro Sekine, Brenda Bushell, Masayuki Goto

    Japan Association for Current English Studies Conference on Current English Studies 

    Presentation date: 2004.10

  • UMLに基づくビジネスモデルの多変量解析による構造分析

    後藤正幸, 増井忠幸

    ビジネスモデル学会 年次大会 

    Presentation date: 2004.10

  • 多教室同時開講型プログラミング演習科目の試みとその効果

    後藤正幸, 大野明彦, 萩原拓郎, 横井利彰

    社)私立大学情報教育協会 平成16年度大学情報化全国大会 

    Presentation date: 2004.09

  • 自然言語表現に基づく学生アンケート分析システム

    酒井哲也, 石田 崇, 後藤正幸, 平澤茂一

    第3回情報科学技術フォーラム FIT 2004 

    Presentation date: 2004.09

  • リハビリテーションデータベース開発(第3報)−活動評価標準化案と分析試行結果−

    小林 順子, 太田 久彦, 大成 尚, 大久保 寛基, 木村 哲彦, 後藤 正幸, 陶山 哲夫

    第54回日本病院学会 

    Presentation date: 2004.07

  • ネパールと連携した環境教育コンテンツの開発と実装

    後藤正幸, ブレンダ・ブッシェル, 柳生修二

    日本経営工学会 平成16年度春季大会 

    Presentation date: 2004.05

  • UMLに基づく物流モデルの情報チェーン分析手法

    今 剛士, 後藤正幸, 増井忠幸

    日本経営工学会 平成16年度春季大会 

    Presentation date: 2004.05

  • 学習モデルに基づいたe-learningコンテンツの設計と評価

    松元崇子, 後藤正幸

    日本経営工学会 平成16年度春季大会 

    Presentation date: 2004.05

  • 学習モデルに基づいたe-learningコンテンツの設計と評価に関する研究

    松元崇子, 後藤正幸

    日本経営工学会西関東支部 第4回学生論文発表会 

    Presentation date: 2004.03

  • UMLに基づく物流モデルの情報チェーン分析手法に関する研究

    今 剛士, 後藤正幸, 増井忠幸

    日本経営工学会西関東支部 第4回学生論文発表会 

    Presentation date: 2004.03

  • 授業モデルとその検証

    石田崇, 伊藤潤, 後藤正幸, 酒井哲也, 平澤茂一

    経営情報学会2003年度秋季全国研究発表大会 

    Presentation date: 2003.11

  • ベイズ統計を用いた文書ファイルの自動分析手法

    後藤正幸, 伊藤潤, 石田崇, 酒井哲也, 平澤茂一

    経営情報学会2003年度秋季全国研究発表大会 

    Presentation date: 2003.11

  • リハビリテーションデータベース開発(1)−Healthcare Qualityとリスク調整アウトカム評価−

    太田 久彦, 小林 順子, 木村 哲彦, 高倉 保幸, 陶山 哲夫, 高橋 邦泰, 後藤 正幸

    リハビリテーション・ケア合同研究大会 

    Presentation date: 2003.10

  • リハビリテーションデータベース開発(2)−経緯と現状について−

    小林 順子, 太田 久彦, 木村 哲彦, 高倉 保幸, 陶山 哲夫, 高橋 邦泰, 後藤 正幸

    リハビリテーション・ケア合同研究大会 

    Presentation date: 2003.10

  • ネットワークを活用した中小企業の技術マーケティング

    後藤正幸, 渡辺法比古, 増井忠幸

    武蔵工業大学環境情報学部シンポジウム-情報の参加デザイン発表概要集 

    Presentation date: 2003.09

  • LAN環境の構築と管理に関する演習授業とその効果

    後藤 正幸, 家木 俊温, 萩原 拓郎

    私立大学情報教育協会 平成15年度大学情報化全国大会 

    Presentation date: 2003.09

  • 授業に関する選択式・記述式アンケートの分析

    平澤茂一, 石田 崇, 伊藤 潤, 後藤 正幸, 酒井哲也

    私立大学情報教育協会 平成15年度大学情報化全国大会 

    Presentation date: 2003.09

  • PLSI を利用した文書からの知識発見

    伊藤潤, 石田崇, 後藤正幸, 平澤茂一

    情報科学技術フォーラム FIT 2003 

    Presentation date: 2003.09

  • メディアタブローの実況連動型使用による協調学習の試みについて

    杉本明日香, 赤間啓之, 大津真知子, 馬越庸恭, 後藤正幸, 高山緑, 山田豊通

    CIEC(コンピュータ利用教育協議会)2003PCカンファレンス 

    Presentation date: 2003.08

  • 選択式・記述式アンケートからの知識発見

    後藤正幸, 酒井哲也, 伊藤潤, 石田崇, 平澤茂一

    CIEC(コンピュータ利用教育協議会) 2003PCカンファレンス 

    Presentation date: 2003.08

  • 情報検索技術を用いた効率的な授業アンケートの分析

    酒井哲也, 伊藤潤, 後藤正幸, 石田崇, 平澤茂一

    経営情報学会2003年春季全国研究発表大会 

    Presentation date: 2003.06

  • ポリマー染色技術によるエコ・ブランド戦略

    渡邊法比古, 薗部祐希, 後藤正幸

    日本環境学会第29回研究発表会 

    Presentation date: 2003.06

  • ベンチャー企業の特許戦略 ―創造的な時間とコスト管理―

    渡邊方比古, 薗部祐希, 土田泰広, 後藤正幸, 藤末健三

    日本知財学会, 第1回研究発表会・シンポジウム 

    Presentation date: 2003.05

  • 語頭条件を満たさない単語集合をもつWord-Valued Sourceの性質について

    石田崇, 後藤正幸, 松嶋敏泰, 平澤茂一

    電子情報通信学会技術研究報告IT2003-5 

    Presentation date: 2003.05

  • Fun and Creativity Creates Huge Profit - Based on The Case Studies of Four Small Businesses -

    渡邊法比古, 薗部祐希, 後藤正幸

    企業家研究フォーラム第1回全国大会 

    Presentation date: 2003.05

  • A New Promotion Model for Small Businesses

    Norihiko Watanabe, Yuki Sonobe, Masayuki Goto, Kenzo Fujisue

    2003年ビジネスモデル学会年次大会 

    Presentation date: 2003.03

  • 単語単位で系列を出力する情報源の性質について

    石田崇, 後藤正幸, 松嶋敏泰, 平澤茂一

    第25回情報理論とその応用シンポジウム 

    Presentation date: 2002.12

  • リハビリテーション診療支援のためのデータベース開発

    太田久彦, 後藤正幸

    第22回医療情報学連合大会 

    Presentation date: 2002.11

  • 統計的考察に基づくAHPの多角的整合性評価について

    浦野真穂, 石田崇, 後藤正幸, 平澤茂一

    日本経営工学会秋季大会 

    Presentation date: 2002.10

  • リンク構造に基づくWebコミュニティの抽出手法

    小森泉, 石田崇, 後藤正幸, 平澤茂一

    日本経営工学会秋季大会 

    Presentation date: 2002.10

  • 生活者の消費と欲求に関する研究

    伊藤朝紀, 後藤正幸, 大槻繁雄, 俵信彦

    日本経営工学会秋季大会 

    Presentation date: 2002.10

  • IDEF0を用いた業務プロセスモデルの定量的評価手法に関する一考察

    明石英樹, 後藤正幸, 俵信彦

    日本経営工学会秋季大会 

    Presentation date: 2002.10

  • クラスタ生成に基づく電子メールの重要度ランク付け手法

    小川恭之, 石田崇, 後藤正幸, 平澤茂一

    情報科学技術フォーラム FIT 2002 

    Presentation date: 2002.09

  • 文間の単語共起類似度を用いた重要文抽出手法

    伊藤潤, 石田崇, 後藤正幸, 平澤茂一

    情報科学技術フォーラム FIT 2002 

    Presentation date: 2002.09

  • グローバル製造業のコストモデルとその実装

    浜本正明, 後藤正幸, 松島克守, 森 雅彦

    2002年ビジネスモデル学会年次大会 

    Presentation date: 2002.03

  • 不完全データを含む分割表におけるベイズ予測

    本田真理, 後藤正幸, 平澤茂一

    第24回情報理論とその応用シンポジウム 

    Presentation date: 2001.11

  • 変分ベイズ法に基づくARモデルによる混合予測について

    倉持佳生, 後藤正幸, 平澤茂一

    第24回情報理論とその応用シンポジウム 

    Presentation date: 2001.11

  • 単語単位で系列を出力する情報源に対するLZ78符号のユニバーサル性について

    石田崇, 後藤正幸, 平澤茂一

    第24回情報理論とその応用シンポジウム 

    Presentation date: 2001.11

  • ブランドエクイティの定量化に関する一提案

    室岡高広, 後藤正幸, 俵 信彦

    日本経営工学会秋季大会 

    Presentation date: 2001.10

  • 行列で表現されたアンケートデータのクラスタリングについて

    和田寛子, 後藤正幸, 平澤茂一

    日本経営工学会秋季大会 

    Presentation date: 2001.10

  • 囚人のジレンマゲームとエントロピーモデルに基づく価格競争モデルの提案

    野田誠, 俵 信彦, 後藤正幸

    日本経営工学会秋季大会 

    Presentation date: 2000.10

  • 質的・量的データが混在する場合の分類問題に関する一考察

    清水裕之, 俵 信彦, 後藤正幸

    日本経営工学会秋季大会 

    Presentation date: 2000.10

  • 感性情報の構造解析に関する研究

    金谷篤, 俵 信彦, 後藤正幸

    日本経営工学会秋季大会 

    Presentation date: 2000.10

  • 属性間の関連を考慮した帰納推論に関する一考察

    岩本佳久, 後藤正幸, 平澤茂一

    日本経営工学会秋季大会 

    Presentation date: 2000.10

  • EMアルゴリズムに基づくAHPの評価値算出法について

    後藤正幸, 松嶋敏泰, 平澤茂一

    2000年情報論的学習理論ワークショップ 

    Presentation date: 2000.07

  • 観測雑音を考慮したARモデルによるベイズ最適な予測法

    倉持佳生, 後藤正幸, 平澤茂一

    電子情報通信学会 技術研究報告 IT2000-17 

    Presentation date: 2000.07

  • パラメータが変動するFSMX情報源に対するベイズ符号化に関する一考察

    鎌須賀敦之, 後藤正幸, 平澤茂一

    電子情報通信学会 技術研究報告 IT2000-16 

    Presentation date: 2000.07

  • 不完全データからのベイズ推定に関する一考察

    本田真理, 後藤正幸, 平澤茂一

    電子情報通信学会 技術研究報告 IT2000-15 

    Presentation date: 2000.07

  • 統計的推定の立場からみたZiv-Lempel符号に関する一考察

    対馬克也, 後藤正幸, 平澤茂一

    第22回情報理論とその応用シンポジウム 

    Presentation date: 1999.12

  • 単語単位で系列を出力する情報源

    後藤正幸, 松嶋敏泰, 平澤茂一

    第22回情報理論とその応用シンポジウム 

    Presentation date: 1999.12

  • 業務プロセスにおけるアクティビティの関連性の定量化手法に関する一研究

    平山宗一郎, 後藤正幸, 俵 信彦

    日本経営工学会秋期大会 

    Presentation date: 1999.10

  • 線形回帰モデルにおけるベイズ最適な予測法に関する研究

    肥土雅生, 後藤正幸, 俵 信彦

    日本経営工学会秋期大会 

    Presentation date: 1999.10

  • 一対比較行列が不完全な場合のAHPに関する研究

    本川 亨, 後藤正幸, 俵 信彦

    日本経営工学会秋期大会, 

    Presentation date: 1999.10

  • On Asymptotic Normality of Density Functions and Its Applications

    GOTO, Masayuki, MATSUSHIMA, Toshiyasu, HIRASAWA Shigeichi

    1999年情報論的学習理論ワークショップ 

    Presentation date: 1999.08

  • 単語単位で出現する系列に対するベイズ符号について

    石田 崇, 後藤正幸, 平澤茂一

    電子情報通信学会 技術研究報告 IT99-28 

    Presentation date: 1999.07

  • 事後確率密度の漸近正規性に関する一考察

    丸山英昭, 後藤正幸, 平澤茂一

    電子情報通信学会 技術研究報告 IT99-27 

    Presentation date: 1999.07

  • モデル族の部分集合に基づく予測について

    島 幸夫, 後藤正幸, 松嶋敏泰, 平澤茂一

    第21回情報理論とその応用シンポジウム 

    Presentation date: 1998.12

  • Extended Stochastic Complexity の漸近式について

    後藤正幸, 松嶋敏泰, 平澤茂一

    第21回情報理論とその応用シンポジウム 

    Presentation date: 1998.12

  • 統計的モデル選択の規準について

    後藤正幸, 松嶋敏泰, 平澤茂一, 俵 信彦

    日本経営工学会秋期大会 

    Presentation date: 1998.11

  • String Matching に基づく有歪み圧縮に関する研究

    前田浩二, 後藤正幸, 平澤茂一

    電子情報通信学会 技術研究報告IT98-29 

    Presentation date: 1998.07

  • 部分的な復号を可能にするLZ78符号の修正符号

    対馬克也, 後藤正幸, 平澤茂一

    電子情報通信学会 技術研究報告IT98-28 

    Presentation date: 1998.07

  • 符号語コストを考慮した情報源符号化について

    吉田隆弘, 後藤正幸, 俵 信彦

    日本経営工学会春季大会 

    Presentation date: 1998.05

  • 事後確率密度の漸近正規性について

    後藤正幸, 松嶋敏泰, 平澤茂一

    日本経営工学会春季大会 

    Presentation date: 1998.05

  • 階層モデル族のモデル選択における選択誤り率について

    後藤正幸, 松嶋敏泰, 平澤茂一

    電子情報通信学会 技術研究報告IT97-64 

    Presentation date: 1998.01

  • 木構造モデル族のモデル選択法に関する一考察

    中尾 峰, 後藤正幸, 松嶋敏泰, 平澤茂一

    第20回情報理論とその応用シンポジウム 

    Presentation date: 1997.12

  • 指数分布に対する事前分布について 〜 寿命分布に対するベイズ推定量に関する研究 〜

    青木美穂, 後藤正幸, 俵 信彦

    日本経営工学会 秋季大会 

    Presentation date: 1997.11

  • 在庫量・発注量変動によるコスト増を考慮した定期発注方式に関する研究

    西嶋 淳, 後藤正幸, 俵 信彦

    日本経営工学会 秋季大会 

    Presentation date: 1997.11

  • 分割表における母数推定に関する研究

    菊池淑子, 後藤正幸, 俵 信彦

    日本経営工学会 秋季大会 

    Presentation date: 1997.11

  • 線形回帰モデルのベイズ最適な予測法に関する研究

    鈴木友彦, 後藤正幸, 俵 信彦

    日本経営工学会 秋季大会 

    Presentation date: 1997.11

  • 木構造のモデル族の学習・予測アルゴリズムに関する一考察

    島 幸夫, 後藤正幸, 松嶋敏泰, 平澤茂一

    電子情報通信学会 技術研究報告 IT97-36 

    Presentation date: 1997.07

  • ベイズ決定理論に基づく統計的モデル選択について

    後藤正幸, 松嶋敏泰, 平澤茂一

    電子情報通信学会 技術研究報告 IT97-21 

    Presentation date: 1997.07

  • String Matching Algorithm による有歪み圧縮について

    小幡洋昭, 後藤正幸, 平澤茂一

    電子情報通信学会 技術研究報告 IT96-60 

    Presentation date: 1997.01

  • Clarke と Barron の Bayesian Asymptotics について

    後藤正幸, 松嶋敏泰, 平澤茂一

    電子情報通信学会 技術研究報告 IT96-59 

    Presentation date: 1997.01

  • マルコフ情報源に対するVF符号に関する一考察

    木村 勝, 後藤正幸, 松嶋敏泰, 平澤茂一

    第19回情報理論とその応用シンポジウム 

    Presentation date: 1996.12

  • 異なる事前分布を持つ場合のMDL原理に基づく符号とベイズ符号の符号長に関する一考察

    後藤正幸, 松嶋敏泰, 平澤茂一

    第19回情報理論とその応用シンポジウム 

    Presentation date: 1996.12

  • 母集団のパラメータ変化時点検出のベイズアプローチに関する一考察

    山下公子, 後藤正幸, 俵 信彦

    日本経営工学会 秋季大会 

    Presentation date: 1996.10

  • 符号長を評価とした確率モデルの学習に関する一考察

    後藤正幸, 松嶋敏泰, 平澤茂一, 俵 信彦

    日本経営工学会 秋季大会 

    Presentation date: 1996.10

  • 統計的モデル選択問題における選択誤り確率に関する一考察

    中尾 峰, 後藤正幸, 松嶋敏泰, 平澤茂一

    電子情報通信学会 技術研究報告 IT96 

    Presentation date: 1996.07

  • MDL基準に基づく符号とベイズ符号の符号長に関する解析

    後藤正幸, 松嶋敏泰, 平澤茂一

    電子情報通信学会技術研究報告 IT96-3 

    Presentation date: 1996.05

  • 共役勾配法における探索効率向上法について

    吉田隆弘, 後藤正幸, 俵 信彦

    日本経営工学会 春季大会 

    Presentation date: 1996.05

  • 情報源の変化時点検出におけるMDL原理とベイズ理論について

    後藤正幸, 松嶋敏泰, 平澤茂一, 俵 信彦

    日本経営工学会 春季大会 

    Presentation date: 1996.05

  • 階層型ニューラルネットワークの混合モデルによるベイズ最適な予測について

    橋川弘紀, 後藤正幸, 俵 信彦

    電子情報通信学会 技術研究報告 NC95 -121 

    Presentation date: 1996.03

  • 最適制御を用いた発注方式の生産−在庫システムへの適用

    内園みどり, 後藤正幸, 俵 信彦

    日本経営工学会 秋季大会 

    Presentation date: 1995.11

  • 確率モデルの学習に関する一考察

    後藤正幸, 松嶋敏泰, 平澤茂一, 俵 信彦

    日本経営工学会 秋季大会 

    Presentation date: 1995.11

  • 観測雑音を考慮した不確実な知識の推論法について

    川又英紀, 後藤正幸, 松嶋敏泰, 平澤茂一

    第18回情報理論とその応用シンポジウム 

    Presentation date: 1995.10

  • ユニバーサル情報源符号化アルゴリズムに基づく事後確率推定アルゴリズム

    阿部真士, 後藤正幸, 松嶋敏泰, 平澤茂一

    第18回情報理論とその応用シンポジウム 

    Presentation date: 1995.10

  • ベイズ決定理論に基づくデータ解析に関する一考察

    後藤正幸, 松嶋敏泰, 平澤茂一

    第18回情報理論とその応用シンポジウム 

    Presentation date: 1995.10

  • 汎化を考慮した確率モデルの学習について

    後藤正幸, 松嶋敏泰, 平澤茂一

    情報処理学会全国大会 

    Presentation date: 1995.09

  • 情報源モデルのパラメータ推定とZiv-Lempel符号について

    木村 勝, 後藤正幸, 松嶋敏泰, 平澤茂一

    電子情報通信学会 技術研究報告 IT95-21 

    Presentation date: 1995.07

  • 情報量基準による階層型ニューラルネットワークのモデル構造決定方法

    開沼泰隆, 橋川弘紀, 後藤正幸, 俵 信彦

    日本経営工学会 春季大会 

    Presentation date: 1995.05

  • 有色雑音を持つ確率システムのフィードバック制御と生産-在庫システムへの適用

    後藤正幸, 内園みどり, 俵 信彦

    日本経営工学会 春季大会 

    Presentation date: 1995.05

  • 2値分類問題へのニューラルネットワークの適用に関する一考察

    松原徳明, 後藤正幸, 松嶋敏泰, 平澤茂一

    第17回情報理論とその応用シンポジウム 

    Presentation date: 1994.12

  • エントロピー最小化によるフィードバック制御の定式化

    後藤正幸, 平澤茂一, 俵 信彦

    第17回情報理論とその応用シンポジウム 

    Presentation date: 1994.12

  • 学習アルゴリズムが与える汎化への影響について

    後藤正幸, 橋川弘紀, 俵 信彦

    日本経営工学会 秋季大会 

    Presentation date: 1994.11

  • 二次近似範囲を調整する共役勾配法とBP学習への適用

    後藤正幸, 俵 信彦

    電子情報通信学会 技術研究報告 NC94-37 

    Presentation date: 1994.10

  • 階層型ニューラルネットワークにおける入力変数の指定法

    橋川弘紀, 後藤正幸, 俵 信彦

    日本経営工学会 春季大会 

    Presentation date: 1994.05

  • 共役勾配法によるBP学習について

    後藤正幸, 開沼泰隆, 俵 信彦

    日本経営工学会 秋季大会 

    Presentation date: 1993.11

  • A Fast Learning Algorithm for Multilayer Neural Networks

    Yasutaka Kainuma, Masayuki Gotoh, Nobuhiko Tawara

    ICPR Production Research 93 (Finland) 

    Presentation date: 1993.08

  • バックプロパゲーション学習アルゴリズムについて

    開沼泰隆, 後藤正幸, 俵 信彦

    日本経営工学会春季大会 

    Presentation date: 1993.05

▼display all

Research Projects

  • Construction of an analysis models for utilizing large-scale logs and experimental data to realize a data-driven enterprise system

    Japan Society for the Promotion of Science  Grants-in-Aid for Scientific Research (A)

    Project Year :

    2024.04
    -
    2028.03
     

    Masayuki Goto, Masao Ueda, Takeshi Moriguchi, Yoichi Seki, Hideo Suzuki, Takashi Namatame, Manabu Kobayashi, Kenta Mikawa, Haruka Yamashita

  • A study on effective measure verification based on a reinforcement learning approach to maximize marketing effectiveness

    Japan Society for the Promotion of Science  Grants-in-Aid for Scientific Research

    Project Year :

    2024.04
    -
    2027.03
     

  • Design and Evaluation Methods for Agriculture to Food (A2F) Value Network System Based on Self-Distribution

    Japan Society for the Promotion of Science  Grants-in-Aid for Scientific Research

    Project Year :

    2023.04
    -
    2027.03
     

    Takahiro Ohno, Yoshikuni Edagawa, Kotomichi Matsuno, Shunichi Ohmori, Takashi Hasuike, Masayuki Goto

  • Development and Empirical Evaluation of Next-generation Experimental Design Technology as Basis for Data-driven Society

    Japan Society for the Promotion of Science  Grants-in-Aid for Scientific Research

    Project Year :

    2021.04
    -
    2025.03
     

    Masayuki Goto, Masao Ueda, Takeshi Moriguchi, Yoichi Seki, Hideo Suzuki, Takashi Namatame, Manabu Kobayashi, Kenta Mikawa, Haruka Yamashita

  • ポストコロナ時代における異文化ネットワーキングに基づく協働型主体的学習モデル

    日本学術振興会  科学研究費助成事業 挑戦的研究(萌芽)

    Project Year :

    2021.07
    -
    2024.03
     

    後藤 正幸, 杉原 真晃, 山下 遥

     View Summary

    本研究では,ネパールをフィールドとして展開する「学生主体型海外連携教育プログラム」を実証的評価の場とし,ポストコロナ社会を想定した異分野・異文化協働型の主体的学習モデルを提案することを目的としている。
    2021年度は,新型コロナウイルス感染症の影響を受け,日本側の研究チームや学生達の海外出張,並びにネパール学生の来日プログラムが実施できない状況に加え,日本とネパールの双方でコロナ禍の社会状況が大きく変動し,当初予定していた研究交流活動が困難となった。このような状況下,我々の研究チームでは,2021年8月にネパールと日本をオンラインでつないで,リアルタイムのオンラインシンポジウムを企画して実施し,一定の成果を得ることができた。このオンラインシンポジウムでは,お互いの国の新型コロナウイルス感染症の影響や社会動向について,双方の大学生が調査し,プレゼンテーションを行い,活発な意見交換が行われた。その成果として,日本とネパール間のリアルタイムの通信環境も問題とはならず,有効な学生交流手段となり得ることが明らかとなった。
    また,2021年12月に,スリランカの女性たちが立ち上げたソーシャルビジネスを題材に,現地の様子を取材したスリランカ人ジャーナリストから報告を受け,日本の大学生ならびに社会人が参加するオンラインディスカッションを実施した。本研究におけるネパールとのオンラインでの交流・研究のヒントになるものとして位置づけ,様々な知見を得ることができた。
    オンライン形式で実施したシンポジウムのノウハウや経験は,今後の日本-ネパールの両国を繋いだ教育プログラムの設計に対して様々な知見を提供してくれており,今後の研究の発展に結びける予定である。特に,両国の交流が戻った後においても,対面でのフィールドプログラムの前後において,オンライン形式での研究会や勉強会が教育効果を発揮することが期待できる。

  • A Study on Environmental-friendly Business Models for Sustainable Development in Nepal

    Japan Society for the Promotion of Science  Grants-in-Aid for Scientific Research Grant-in-Aid for Scientific Research (B)

    Project Year :

    2013.04
    -
    2018.03
     

    BUSHELL Brenda

     View Summary

    As Nepal industrializes, environmental sustainability is regarded as one of the pressing challenges. This research analyzed the potential for environmental-friendly business models in urban and rural Nepal, focusing on sectors of tourism, agriculture and informal enterprise. We found that mobilizing linkages among these sectors and adopting ICT can support and promote eco-friendly sustainable business models, particularly for small scale businesses run by women. Additionally, perspectives from local citizens reveal the importance of community support systems, such as waste management, Education for Sustainable Development (ESD) and health promotion. The research illustrates the need to include gender issues, as well as the wealth and education gap. There is also a need for coordinated efforts from businesses and government in developing the social and economic systems that work in harmony with the environmental systems to support sustainable development in the business sector.

  • Development and application of next generation pattern recognition methods for user behavior analysis using large scale log data

    Japan Society for the Promotion of Science  Grants-in-Aid for Scientific Research Grant-in-Aid for Challenging Exploratory Research

    Project Year :

    2014.04
    -
    2017.03
     

    GOTO Masayuki

     View Summary

    The objective of this study is to develop next-generation pattern recognition methods for management decision making and marketing technology through the analysis of user behaviors based on large-scale log data accumulated in databases such as EC sites. While analyzing actual user behavior history data, we studied new models and methods which are theoretically considered highly versatile. In particular, we developed a machine learning model for developing the issuing logic for real-time coupon ticket systems from multiple viewpoints on users' page browsing behaviors on an EC site. The performances of our proposed models and analytical techniques were verified by using actual data and various applications were demonstrated.

  • Fundamental study on business analytics technologies on big data era

    Japan Society for the Promotion of Science  Grants-in-Aid for Scientific Research Grant-in-Aid for Scientific Research (B)

    Project Year :

    2014.04
    -
    2017.03
     

    Goto Masayuki

     View Summary

    The objective of this study is to develop and deepen large-scale and diverse business data analytical technology (business analytics), propose new analytical models corresponding to various business data.
    Specifically, we promoted research on the following individual themes: 1) development of data analytics technology for database information on EC sites, 2) development of analytical technique of marketing information accumulated as text data, 3) development of statistical model for recommendar systems, 4) Theoretical analysis of Web marketing model using information retrieval and recommendation technology, 5) Development of analytical method for high dimensional and sparse large scale data, 6) Development of privacy protection data analysis technology.

  • A Fundamental Study on Web Marketing Technology on Social Media Era

    Japan Society for the Promotion of Science  Grants-in-Aid for Scientific Research Grant-in-Aid for Scientific Research (C)

    Project Year :

    2011
    -
    2013
     

    GOTO Masayuki

     View Summary

    In this research, the information analytics and the Web marketing technology were studied for supporting the business activities on the social media era. Focusing on the fundamental techniques and models for analytics and Web marketing tools, several new models to analyze the text data and statistical models have been proposed. The new model for recommendation system and statistical models to represent the consumer behaviors have also been studied. Through numerical experiments and applications to the real data, the effectiveness of the proposed models and methods were clarified. Moreover, the asymptotic analysis of the high dimensional vector space model of text data with sparseness was conducted. The asymptotic optimality of the distance measures on the text vector space was shown.

  • Text Mining for Languages of All Ages and Countries

    Japan Society for the Promotion of Science  Grants-in-Aid for Scientific Research Grant-in-Aid for Scientific Research (C)

    Project Year :

    2010
    -
    2012
     

    SUZUKI Makoto, OHSUGA Akihiko, GOTO Masayuki, SUKO Tota

     View Summary

    We proposed the accumulation method, which is a language-independent text classification method that is based on the character N-gram. The accumulation method does not depend on the language structure, because this method uses the character N-gram to form index terms. If text documents are expressed in Unicode, then the accumulation method can classify documents using the same algorithm. Therefore, we classified English, Japanese, Korean, and Chinese text documents. As a result, the highest macro-averaged F-measures of the proposed method were 94.5% for the English Reuters-21578, 88.5% for the Japanese CD-Mainichi 2002 data set, 90.2% for the Korean Hankyoreh 2008 data set, and 92.6% for the People's Daily 2009-2010 data set. Thus, we obtained good results for these languages. Moreover, we were able to construct a mathematical model of the accumulation method and were able to clarify the mathematical meaning.

  • Research into the construction of a sustainable community model in Nepal, based on a system of community networking

    Japan Society for the Promotion of Science  Grants-in-Aid for Scientific Research Grant-in-Aid for Scientific Research (B)

    Project Year :

    2008
    -
    2012
     

    BRENDA Bushell, GOTO Masayuki, YOSHIDA Kuniko, OKADA Akira, HOM BAHADUR Rijal

     View Summary

    The research activities over the past five years have resultedin the development of a network of community members who share the goal of sustainable community development. By researching deeply about environmental, social and economic issues facing communities in both rural and urban Nepal, the research has identified 65 community indicators in which community stakeholders believe are important for the creation of a sustainable community. These indicators were developed and piloted from 2009 to 2012. The research also identified the following community members as the networking community: 1) households, including both male and female household members; 2) shopkeepers; 3) government and private schools; 4) NGOs, including World Wildlife Fund, 5) Biogas organizations; and 6) Community women’s organizations. Through focus group discussions and surveys with each of the above members, it was found that linking these members through a community networking system can support the development of a sustainable community.

  • A Study on Information Structuring Method for Problems in the Field of Industrial Engineering and Management

    Japan Society for the Promotion of Science  Grants-in-Aid for Scientific Research Grant-in-Aid for Scientific Research (C)

    Project Year :

    2008
    -
    2010
     

    GOTO Masayuki

     View Summary

    In this research, information structuring methods were studied from the viewpoint of industrial engineering and management. The several methods to extract knowledge from text data were proposed and the theoretical analysis for the general model was conducted. Moreover, the method to give the strategy map in a structured data base is proposed. On the keyword of "information structuring", various methods that are useful in business scenes were studied and the effectiveness of these methods were clarified by applications to various problems.

  • Design and Evaluation for Environmental Logistics Systems with Economic Synergy

    Japan Society for the Promotion of Science  Grants-in-Aid for Scientific Research Grant-in-Aid for Scientific Research (B)

    Project Year :

    2007
    -
    2009
     

    MASUI Tadayuki, GOTO Masayuki, YAMADA Tetsuo

     View Summary

    This study is a fundamental research for establishing a new environmental logistic-consume paradigm to bridge the environmental aspect and economic aspect. The main findings are to make possible to grasp and show the CO_2 emissions for each delivery good dynamically in logistics process by developing an information system by combining a fuel gauge and IC tags. Also, on the viewpoint of the environmental marketing, a management strategy for logistics companies was discussed by analyzing the relationship among the customer sales rewards, the fuel costs of truck and the CO_2 volumes in the transportation processes.

  • A Study on Knowledge Representation Model for Small and Medium Business Management and Its Structural Analysis Method

    Japan Society for the Promotion of Science  Grants-in-Aid for Scientific Research Grant-in-Aid for Young Scientists (B)

    Project Year :

    2006
    -
    2007
     

    Masayuki Goto

  • Construction and Development of an Environmental Education Module based on Knowledge Gathered in Nepal

    Japan Society for the Promotion of Science  Grants-in-Aid for Scientific Research Grant-in-Aid for Scientific Research (B)

    Project Year :

    2005
    -
    2007
     

    BUSHELL Brenda, YOSHIDA Kuniko, GOTO Masayuki

     View Summary

    The purpose of the research from 2005 to 2007 was to identify key environment issues in Nepal, and, through various approaches in gathering the information, construct and develop a learning module for environment education. As the impetus for environmental action originates at the grassroots level in Nepal, 5 main sectors were targeted as representative of holding knowledge about and taking action in and for the environment. The sectors included ; educational institutions, NGOs, informal grassroots organizations and local communities located in Kathmandu Valley.
    In 2005 core collaboration was established between students and teachers of Musashi Institute of Technology Japan and National College, Kathmandu. An environment education program was designed for grade 6 students at Prabhat Government School. This enabled the gathering of data on the children's environmental awareness and behavior for waste management and sanitation. Further investigation resulted in a partnership between these 3 educational institutions and Zero Waste Nepal, an NGO targeting waste management. Further expanding the research in waste management, specifically in the management of paper recycling and plastic waste management, two women's organizations were targeted ; Jamarko and Women in Sustainable Development. Data was gathered on retail and consumer environmental behavior in relation to their environment management activities. Continued research in 2006 and 2007 focused on the waste transfer system developed by JICA, and the individual and community composting system designed and implemented by the NGO Clean Energy Nepal. Further exchange of information took place in symposiums hosted in Japan in 2006 and 2007. In total three types of learning contents were recorded on DVD and evaluated and web pages in both English and Japanese were created as learning tools for environment education. Embedded in the research was a student-centered study program in which students learned to take leadership and responsibility for their learning, based on action research.

  • A Study on Construction of Case Study Type E-learning Environment implementing Corporate Simulator

    Japan Society for the Promotion of Science  Grants-in-Aid for Scientific Research Grant-in-Aid for Young Scientists (B)

    Project Year :

    2003
    -
    2004
     

    GOTO Masayuki

  • A Study of Information Chain for Realization of Informative Logistics System

    Japan Society for the Promotion of Science  Grants-in-Aid for Scientific Research Grant-in-Aid for Scientific Research (B)

    Project Year :

    2003
    -
    2004
     

    MASUI Tadayuki, GOTO Masayuki

     View Summary

    In these days, the Supply Chain Management is being took notice in physical distribution activities in the world. As progressing of Information Technology, physical distribution activities are about to focus on not only the flow of goods, but also the flow of information. Today, it is not too much to say that transporting the information smoothly and surely and information sharing between companies is critical in the physical distribution business. Thus, it is necessary to show clearly the flow of information and physical distribution for the Supply Chain Management. We must clearly identify the path of information flow for the purpose of transporting information between the activities and many companies. This flow of information in the way of physical distribution comprises is defining features in the "Information Chain". Due to the development in Information Technology, media for communicating information, for example, IC-tags, two dimensional pattern codes and others have emerged, thus it is important to discuss how to use the information media effectively.
    In this study, we propose a model description method to define the Information Chain. This method can express required information contents, its flow path, information characteristics and Source and Sink of the information. By using this method, we can analyze how to transport the information contents and how to select the information media for each. In the field study, we investigated three companies and represent real Information Chain by our proposal. Moreover, we surveyed the specialist's recognition and analyzed future goals for the physical distribution process. At last, we proposed several new distribution models and clarified that effectiveness by simulation experiments.

  • A Study on New Business Models of the Manufacturing Industry under a Digital Economical Environment

    Japan Society for the Promotion of Science  Grants-in-Aid for Scientific Research Grant-in-Aid for Scientific Research (B)

    Project Year :

    2001
    -
    2002
     

    MATSUSHIMA Katsumori, MIMA Hideki, GOTO Masayuki, FUIISUE Kenzo

     View Summary

    The objective of this research project is to establish new business models of the manufacturing industry under a digital economical environment. In order to realize the objective, the following studies have been carried out.
    (1) A Design and Development of the Global Cockpit : With recent drastic changes of the global economical environment, scientific management is expected to achieve efficient business management. "Global Cockpit" is a management simulator which uses real business environments including global economical environments, such as foreign exchange, business conditions, etc., and a local environment, such as sales, etc. and gives simulated information for aid for managers to make rapid business decision. The main feature of the system is to give a virtual "Cockpit" for driving a business based on the virtual reality techniques. Seamless GUIs with metaphoric icons and a surrounding stereoscopic screen with three projectors ware developed to realize the cockpit. The system was tested by a group of real users.
    (2) Optimization of Global Supply Chain Management : Since current global economical environment changes rapidly day by day, rapid decision making is a key issue to succeed business. Efficient supply chain management is one of the most important issues for managing manufacturing industry. Thus, our approach to accelerate decision is to develop a model for optimizing global supply chain management. Our model is able to handle unstable data such as foreign exchange, and domain specific features such as tax and so on. Evaluation using real Chinese data showed us that the proposed model was feasible and robust enough even in unstable current global market.
    (3) Knowledge Structuring : Recent industry is rapidly turning into the knowledge-based. Consequently, business managers aim at realizing the knowledge-based company. In order to support these movements, we focused on two main approaches, ontology development and knowledge structuring. An automatic term recognition system and an automatic term clustering system using natural language processing techniques have been developed for automatic development of ontology. Knowledge retrieving and structuring system have also been developed for aid to exploit knowledge (ontology) to achieve efficient knowledge-based industry.

▼display all

Misc

  • Online Flipped Conference Based Data Science Education Program and Its Educational Effectiveness in Multi-University Collaboration

    Masayuki Goto, Manabu Kobayashi, Takeshi Moriguchi, Yoichi Seki, Hideo Suzuki, Takashi Namatame, Kazuhide Nakata, Aya, Ishigaki, Masao Ueda, Kimitoshi Sato, Kenta Mikawa, Haruka Yamashita, Tomoaki Tabata, Tianxiang Yang, Ayako Yamagiwa, Yutaka Tajiri

    The 7th Asian Conference of Management Science and Application   (ACMSA2023)  2023.12

    Authorship:Lead author, Corresponding author

    Article, review, commentary, editorial, etc. (international conference proceedings)  

  • Cross-Lingual Analysis Based on Natural Language Model to Explore Nationality Differences in Traveler Value

    Tianxiang Yang, Hideo Suzuki, Masayuki Goto

    The 7th Asian Conference of Management Science and Application   (ACMSA2023)  2023.12  [Refereed]

    Authorship:Last author

    Article, review, commentary, editorial, etc. (international conference proceedings)  

  • Target-awared Source Data Selection Strategy for Transfer Learning

    Kanyu Miyoshi, Ryotaro Shimizu, Linxin Song, Masayuki Goto

    The 7th Asian Conference of Management Science and Application   (ACMSA2023)  2023.12  [Refereed]

    Authorship:Last author

    Article, review, commentary, editorial, etc. (international conference proceedings)  

  • Follow-up survey on the effect of the educational program applying the concept of active learning in the field of Nepal

    Haruka Yamashita, Manita Shresta, Masaaki Sugihara, Masayuki Goto

    The 7th Asian Conference of Management Science and Application   (ACMSA2023)  2023.12  [Refereed]

    Authorship:Last author

    Article, review, commentary, editorial, etc. (international conference proceedings)  

  • A Clustering Method Using Embedded Representations Based on User Ratings

    Miho Mizutani, Ayako Yamagiwa, Masayuki Goto

    The 7th Asian Conference of Management Science and Application   (ACMSA2023)  2023.12  [Refereed]

    Authorship:Last author

    Article, review, commentary, editorial, etc. (international conference proceedings)  

  • Multi-Task Learning for Estimating Consumer Impressions of Product Images

    Ayako Yamagiwa, Masayuki Goto

    The 7th Asian Conference of Management Science and Application   (ACMSA2023)  2023.12  [Refereed]

    Authorship:Last author

    Article, review, commentary, editorial, etc. (international conference proceedings)  

  • A Predictive Model of User Attributes from Action History Data based on VIME

    Mizuki Takeuchi, Yuta Sakai, Masayuki Goto

    23rd Asia Pacific Industrial Engineering & Management System Conference   (APIEMS 2023)  2023.10  [Refereed]

    Authorship:Last author

    Article, review, commentary, editorial, etc. (international conference proceedings)  

  • Selection Criteria of Evaluation Axes for Impression Map Visualizations of Product Images

    Ayako Yamagiwa, Masayuki Goto

    23rd Asia Pacific Industrial Engineering & Management System Conference   (APIEMS 2023)  2023.10  [Refereed]

    Authorship:Last author

    Article, review, commentary, editorial, etc. (international conference proceedings)  

  • Experimental Design based on Machine Learning for Finding Customer Groups with High Measure Effects

    Yuka Nakamura, Ayako Yamagiwa, Hokuto Sasaki, Masayuki Goto

    23rd Asia Pacific Industrial Engineering & Management System Conference   (APIEMS 2023)  2023.10  [Refereed]

    Authorship:Last author

    Article, review, commentary, editorial, etc. (international conference proceedings)  

  • Analysis of Purchase History Data and Reviews in Embedded Space

    Yuto Nunome, Yuta Sakai, Masayuki Goto

    23rd Asia Pacific Industrial Engineering & Management System Conference   (APIEMS 2023)  2023.10  [Refereed]

    Authorship:Last author

    Article, review, commentary, editorial, etc. (international conference proceedings)  

  • A Novel Latent Class Model for Estimating the Relationship between User Preferences and Item Image Features

    Kirin Tsuchiya, Ryotaro Shimizu, Masayuki Goto

    23rd Asia Pacific Industrial Engineering & Management System Conference   (APIEMS 2023)  2023.10  [Refereed]

    Authorship:Last author

    Article, review, commentary, editorial, etc. (international conference proceedings)  

  • An Efficient Active Learning Approach based on BERT for Job Label Classification Problem

    Koki Yamada, Ayako Yamagiwa, Goto Masayuki

    23rd Asia Pacific Industrial Engineering & Management System Conference   (APIEMS 2023)  2023.10  [Refereed]

    Authorship:Last author

    Article, review, commentary, editorial, etc. (international conference proceedings)  

  • Machine Learning Based Experimental Design and Effectiveness Verification for Coupon Measures with Different Discount Amounts

    Akiko Yoneda, Ryotaro Shimizu, Shion Sakurai, Makoto Kawata, Haruka Yamashita, Masayuki Goto

    23rd Asia Pacific Industrial Engineering & Management System Conference   (APIEMS 2023)  2023.10  [Refereed]

    Authorship:Last author

    Article, review, commentary, editorial, etc. (international conference proceedings)  

  • An Improved Algorithm based on Self-supervised Learning for Multi-tag Prediction of Extreme Weather Events

    Tomoki Amano, Ryotaro Shimizu, Masayuki Goto, Tomohiro Yoshikai

    23rd Asia Pacific Industrial Engineering & Management System Conference   (APIEMS 2023)  2023.10  [Refereed]

    Authorship:Last author

    Article, review, commentary, editorial, etc. (international conference proceedings)  

  • Adversarial Counterfactual Regression with Importance Weighting

    Taichi Imafuku, Yuta Sakai, Masayuki Goto

    23rd Asia Pacific Industrial Engineering & Management System Conference   (APIEMS 2023)  2023.10  [Refereed]

    Authorship:Last author

    Article, review, commentary, editorial, etc. (international conference proceedings)  

  • A Predictive Model of Future Sales Prices for Second-hand Smartphone Devices

    Masaki Masuda, Ayako Yamagiwa, Masayuki Goto

    23rd Asia Pacific Industrial Engineering & Management System Conference   (APIEMS 2023)  2023.10  [Refereed]

    Authorship:Last author

    Article, review, commentary, editorial, etc. (international conference proceedings)  

  • A Review Analysis by Automizing Extraction of Query-focused Summarization

    Daisuke Nakamura, Yuta Sakai, Masayuki Goto

    23rd Asia Pacific Industrial Engineering & Management System Conference   (APIEMS 2023)  2023.10  [Refereed]

    Authorship:Last author

    Article, review, commentary, editorial, etc. (international conference proceedings)  

  • An Embedding based Analysis of Multilingual Reviews for Service Quality Improvement of Accommodations for Foreign Travelers

    Kanta Morimoto, Tianxiang Yang, Masayuki Goto

    23rd Asia Pacific Industrial Engineering & Management System Conference   (APIEMS 2023)  2023.10  [Refereed]

    Authorship:Last author

    Article, review, commentary, editorial, etc. (international conference proceedings)  

  • Data-Selective Adversarial Discriminative Domain Adaptation: Improving Accuracy and Reducing Computational Complexity

    Keigo Kimura, Daisuke Nakamura, Yuta Sakai, Masayuki Goto

    The 21st Asian Network for Quality Congress (ANQ 2023), Vietnam,     JP.39  2023.10  [Refereed]

    Authorship:Last author

    Article, review, commentary, editorial, etc. (international conference proceedings)  

  • Price Factor Analysis Based on Machine Learning for Second-hand Smartphone Market

    Takuya Morikawa, Mizuki Takeuchi, Yuta Sakai, Masayuki Goto

    The 21st Asian Network for Quality Congress (ANQ 2023), Vietnam,     JP.37  2023.10  [Refereed]

    Authorship:Last author

    Article, review, commentary, editorial, etc. (international conference proceedings)  

  • An Approach of Customer Segmentation with SHAP Values Focusing on Individual Feature Influences on Outcome Variable

    Naru Shimizu, Yuka Nakamura, Ayako Yamagiwa, Masayuki Goto

    The 21st Asian Network for Quality Congress (ANQ 2023), Vietnam,     JP.35  2023.10  [Refereed]

    Authorship:Last author

    Article, review, commentary, editorial, etc. (international conference proceedings)  

  • An Approximate Bayes Optimal Algorithm for Correcting Sample Selection Bias for Logistic Regression Models

    Taichi Abe, Tota Suko, Masayuki Goto

    The 21st Asian Network for Quality Congress (ANQ 2023), Vietnam,     JP.31  2023.10  [Refereed]

    Authorship:Last author

    Article, review, commentary, editorial, etc. (international conference proceedings)  

  • A Proposal of Product Image Analysis Model based on CVAE Learning Abstract Information of Product Descriptions

    Ayuno Fuchi, Masaki Masuda, Masakazu Asano, Ayako Yamagiwa, Masayuki Goto

    The 21st Asian Network for Quality Congress (ANQ 2023), Vietnam,     JP.14  2023.10  [Refereed]

    Authorship:Last author

    Article, review, commentary, editorial, etc. (international conference proceedings)  

  • Factor Analysis Framework of Review Data based on BERT and SHAP for Improving User Ratings

    Mamiko Watanabe, Koki Yamada, Ryotaro Shimizu, Satoshi Suzuki, Masayuki Goto

    The 21st Asian Network for Quality Congress (ANQ 2023), Vietnam,     JP.10  2023.10  [Refereed]

    Authorship:Last author

    Article, review, commentary, editorial, etc. (international conference proceedings)  

  • A Study on Recommender Model Based on the Neural Collaborative Ranking Model Utilizing Multiple Implicit Feedback with Ordering Relationships

    Ryuta Matsuoka, Akiko Yoneda, Haruka Yamashita, Masayuki Goto

    The 21st Asian Network for Quality Congress (ANQ 2023), Vietnam,     JP.09  2023.10  [Refereed]

    Authorship:Last author

    Article, review, commentary, editorial, etc. (international conference proceedings)  

  • A Study on Performance Improvement and Interpretability of FTTransformer

    Tokimasa Isomura, Tomoki Amano, Ryotaro Shimizu, Masayuki Goto

    The 21st Asian Network for Quality Congress (ANQ 2023), Vietnam,     JP.08  2023.10  [Refereed]

    Authorship:Last author

    Article, review, commentary, editorial, etc. (international conference proceedings)  

  • An Analysis of Customer Characteristics using Embedding Model with Attention Mechanism Considering Auxiliary Information

    Tatsuya Ishii, Kirin Tsuchiya, Tianxiang Yang, Masayuki Goto

    The 21st Asian Network for Quality Congress (ANQ 2023), Vietnam,     JP.03  2023.10  [Refereed]

    Authorship:Last author

    Article, review, commentary, editorial, etc. (international conference proceedings)  

  • Active Learning Method for Pairwise Comparison Data

    Ayako Yamagiwa, Masayuki Goto

    5th IEEE International Conference on Cybernetics, Cognition and Machine Learning Applications (ICCCMLA 2023), Track-4, No.4, Paper-ID.157, Germany    2023.10

    Authorship:Last author

    Research paper, summary (international conference)  

  • Fashion-Specific Ambiguous Expression Interpretation with Partial Visual- Semantic Embedding

    Ryotaro Shimizu, Takuma Nakamura, Masayuki Goto

    The IEEE/CVF Conference on Computer Vision and Pattern Recognition 2023 (CVPR2023)   ( 23579426 )  2023.06  [Refereed]

    Authorship:Last author

    Research paper, summary (international conference)  

    DOI

  • A Consideration about A Sparse Estimation Method for Polynomial Regression Model with Unknown Maximum Degree

    井上一磨, 井上一磨, 清水良太郎, 須子統太, 後藤正幸

    情報処理学会研究報告(Web)   2023 ( MPS-143 )  2023

    J-GLOBAL

  • A Study on Selection Bias Correction Based on Statistical Decision Theory in Logistic Regression Models

    阿部太一, 須子統太, 後藤正幸

    人工知能学会全国大会論文集(Web)   37th  2023

    J-GLOBAL

  • A Study on High-dimensional Data Visualization Methods Based on Angles

    阪井優太, 三川健太, 後藤正幸

    情報理論とその応用シンポジウム予稿集(CD-ROM)   46th  2023

    J-GLOBAL

  • Adaptive Ranking-based Sample Selection for Weakly Supervised Class-imbalanced Text Classification

    Linxin Song, Jieyu Zhang, Tianxiang Yang, Masayuki Goto

    Findings of the 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP 2022)   10707   1641 - 1655  2022.12  [Refereed]

    Authorship:Last author

     View Summary

    To obtain a large amount of training labels inexpensively, researchers have recently adopted the weak supervision (WS) paradigm, which leverages labeling rules to synthesize training labels rather than using individual annotations to achieve competitive results for natural language processing (NLP) tasks. However, data imbalance is often overlooked in applying the WS paradigm, despite being a common issue in a variety of NLP tasks. To address this challenge, we propose Adaptive Ranking-based Sample Selection (ARS2), a model-agnostic framework to alleviate the data imbalance issue in the WS paradigm. Specifically, it calculates a probabilistic margin score based on the output of the current model to measure and rank the cleanliness of each data point. Then, the ranked data are sampled based on both class-wise and rule-aware ranking. In particular, the two sample strategies corresponds to our motivations: (1) to train the model with balanced data batches to reduce the data imbalance issue and (2) to exploit the expertise of each labeling rule for collecting clean samples. Experiments on four text classification datasets with four different imbalance ratios show that ARS2 outperformed the state-of-the-art imbalanced learning and WS methods, leading to a 2%-57.8% improvement on their F1-score. Our implementation can be found in https://github.com/JieyuZ2/wrench/blob/main/wrench/endmodel/ars2.py.

  • An Extended Model of Bayesian Optimization Method Considering Input-dependent Variance for Decision Support in Business Activities

    Taiga Yoshikawa, Yuta Sakai, Tianxiang Yang, Masayuki Goto

    The 22nd Asia Pacific Industrial Engineering and Management Systems Conference (APIEMS2022)     BM015  2022.11  [Refereed]

    Authorship:Last author

    Research paper, summary (international conference)  

  • Recommendation Item Selection Algorithm Considering the Recommendation Region in the Embedding Space

    Tomoki Amano, Ryotaro Shimizu, Masayuki Goto

    The 22nd Asia Pacific Industrial Engineering and Management Systems Conference (APIEMS2022)     BM020  2022.11  [Refereed]

    Authorship:Last author

    Research paper, summary (international conference)  

  • A New Analytical Model for Product Reviews Based on BERT Feature Extraction

    Koutarou Yamashita, Ayako Yamagiwa, Kyosuke Hasumoto, Masayuki Goto

    The 22nd Asia Pacific Industrial Engineering and Management Systems Conference (APIEMS2022)     BM022  2022.11  [Refereed]

    Authorship:Last author

    Research paper, summary (international conference)  

  • Context Construction Based on Latent Dirichlet Allocation for Contextual Bandit Algorithm

    Ryota Matsunae, Ayako Yamagiwa, Masayuki Goto

    The 22nd Asia Pacific Industrial Engineering and Management Systems Conference (APIEMS2022)     BM021  2022.11  [Refereed]

    Authorship:Corresponding author

    Research paper, summary (international conference)  

  • A Method for Scalable Inference of Hidden semi-Markov Model by Batch Learning

    Yosuke Takao, Ayako Yamagiwa, Haruka Yamashita, Masayuki Goto

    The 22nd Asia Pacific Industrial Engineering and Management Systems Conference (APIEMS2022)     IEM076  2022.11  [Refereed]

    Authorship:Last author

    Research paper, summary (international conference)  

  • Machine Learning Approach with SHAP Values for Analyzing Relationships among Products of Individual Stores

    Kodai Ishikura, Yuta Sakai, Masayuki Goto

    The 22nd Asia Pacific Industrial Engineering and Management Systems Conference (APIEMS2022)     BM023  2022.11  [Refereed]

    Authorship:Last author

    Research paper, summary (international conference)  

  • NoDust: Noisy Data Query Strategy Enhanced by Weak Supervised Learning

    Linxin Song, Tianxiang Yang, Masayuki Goto

    The 22nd Asia Pacific Industrial Engineering and Management Systems Conference (APIEMS2022)     IEM051  2022.11  [Refereed]

    Authorship:Last author

    Research paper, summary (international conference)  

  • Multiple Treatment Effect Estimation for E-commerce Marketing Using Observational Data

    Yuki Tsuboi, Yuta Sakai, Ryotaro Shimizu, Masayuki Goto

    The 22nd Asia Pacific Industrial Engineering and Management Systems Conference (APIEMS2022)     IEM010  2022.11  [Refereed]

    Authorship:Last author

    Research paper, summary (international conference)  

  • A Method for Embedding Representations of Customer Feelings for Product Images

    Ayako Yamagiwa, Masayuki Goto

    The 22nd Asia Pacific Industrial Engineering and Management Systems Conference (APIEMS2022)     MRI005  2022.11  [Refereed]

    Authorship:Last author

    Research paper, summary (international conference)  

  • Efficient Effective Path Search Algorithm for Explainable Recommendation Based on Reinforcement Learning

    Guanyu Yang, Ryotaro Shimizu, Ayako Yamagiwa, Masayuki Goto

    The 22nd Asia Pacific Industrial Engineering and Management Systems Conference (APIEMS2022)     BM015  2022.11  [Refereed]

    Authorship:Last author

    Research paper, summary (international conference)  

  • Learning and Estimation of Latent Structural Models Based on Between-Data Metrics

    Kenta Mikawa, Manabu Kobayashi, Masayuki Goto, Shigeichi Hirasawa

    2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC)     Paper We-PS2-T12.5  2022.10  [Refereed]

    Research paper, summary (international conference)  

    DOI

  • Construction Methods for Error Correcting Output Codes Using Constructive Coding and Their System Evaluations

    Shigeichi Hirasawa, Gendo Kumoi, Hideki Yagi, Manabu Kobayashi, Masayuki Goto, Hiroshige Inazumi

    International Conference on Systems, Man, and Cybernetics (IEEE SMC2022)     Paper We-PS2-T11.1  2022.10  [Refereed]

    Research paper, summary (international conference)  

    DOI

  • A Study on Search Problem Based on Bayesian Optimization to Control Worsening of Objective Function Value

    Yuka Nakamura, Taiga Yoshikawa, Ayako Yamagiwa, Masayuki Goto

    The 20th Asian Network for Quality Congress (ANQ2022) ANQ2022, Beijing     ANQ-TQS-22-005  2022.10  [Refereed]

    Authorship:Last author

    Research paper, summary (international conference)  

  • A Method to Improve Serendipity of Recommendation Lists Based on Collaborative Metric Learning

    Akiko Yoneda, Ryota Matsunae, Haruka Yamashita, Masayuki Goto

    The 20th Asian Network for Quality Congress (ANQ2022) ANQ2022, Beijing     ANQ-TQS-22-006  2022.10  [Refereed]

    Authorship:Last author

    Research paper, summary (international conference)  

  • A Method for Item Analysis Considering Duration of User Interests Based on Hidden Semi-Markov Model

    Kirin Tsuchiya, Yuki Tsuboi, Ryotaro Shimizu, Masayuki Goto

    The 20th Asian Network for Quality Congress (ANQ2022) ANQ2022, Beijing     ANQ-TQS-22-014  2022.10  [Refereed]

    Authorship:Last author

    Research paper, summary (international conference)  

  • A Topic Transition Model of Parents Concerns over Child-rearing Stages based on Question Data on Dedicated App

    Koki Yamada, Yosuke Takao, Ayako Yamagiwa, Masayuki Goto

    The 20th Asian Network for Quality Congress (ANQ2022) ANQ2022, Beijing     ANQ-TQS-22-016  2022.10  [Refereed]

    Authorship:Last author

    Research paper, summary (international conference)  

  • A Semi-Supervised Learning Model for Predicting User Attributes Based on Ladder Network

    Mizuki Takeuchi, Taichi Imafuku, Yuta Sakai, Masayuki Goto

    The 20th Asian Network for Quality Congress (ANQ2022) ANQ2022, Beijing     ANQ-TQS-22-022  2022.10  [Refereed]

    Authorship:Last author

    Research paper, summary (international conference)  

  • Data Augmentation Based Method for Enhancing Interpretability of Biterm Topic Model

    Tianxiang Yang, Yuki Nishida, Haruka Yamashita, Masayuki Goto

    The 20th Asian Network for Quality Congress (ANQ2022) ANQ2022, Beijing     ANQ-TQS-22-044  2022.10  [Refereed]

    Authorship:Last author

    Research paper, summary (international conference)  

  • Evaluation of Analysis Model for Products with Coefficients of Binary Classifiers and Consideration of Way to Improve

    Ayako Yamagiwa, Masayuki Goto

    Proceeding of 24th International Conference on Human-Computer Interaction (HCI International 2022)    2022.06  [Refereed]

    Authorship:Last author

    Research paper, summary (international conference)  

    DOI

  • Purchasing Behavior Analysis Model that Considers the Relationship Between Topic Hierarchy and Item Categories

    Yuta Sakai, Yui Matsuoka, Masayuki Goto

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)   13316 LNCS   344 - 358  2022.06  [Refereed]

    Authorship:Last author

     View Summary

    With the spread of EC sites, it has become an important work for companies to analyze user preferences contained in accumulated purchase history data and utilize them in marketing measures. A topic model is well known as a method for analyzing user preferences from purchase history data, and a model assuming hierarchy of topics has been proposed as an extension method. The previously proposed PAM (Pachinko Allocation Model) is a highly expressive model in which all upper and lower topics are connected by a network and the relationships between multiple topics can be analyzed. However, PAM is easily affected by the initial values of learning parameters, and it is difficult to obtain stable topics, so the interpretation of the estimated topics becomes unstable. It is dangerous to make business decisions based on the interpretation of such unstable results. Therefore, in this research, instead of using the hierarchy of topics estimated based on the user’s purchasing behavior, we use information with a hierarchical structure of “product categories” given by the EC site for managing items. Therefore, we propose a method that is useful for studying measures and that enables hierarchical topic analysis. Finally, the proposed method is applied to the evaluation history data of the actual EC site to analyze the user’s preference and show its usefulness.

    DOI

  • Performance Evaluation of ECOC Considering Estimated Probability of Binary Classifiers

    Gendo Kumoi, Hideki Yagi, Manabu Kobayashi, Masayuki Goto, Shigeichi Hirasawa

    Lecture Notes in Networks and Systems   469 LNNS   379 - 389  2022.04  [Refereed]

     View Summary

    Error-Correcting Output Coding (ECOC) is a method for constructing a multi-valued classifier using a combination of the given binary classifiers. ECOC is said to be able to estimate the correct category by other binary classifiers even if the output of some binary classifiers is incorrect based on the framework of the coding theory. Although it is experimentally known that this method performs well on real data, a theoretical analysis of the classification accuracy for ECOC has yet to be conducted. In this study, we evaluate the superiority of a code word table in showing the combinations of binary classifiers of ECOC that have been experimentally demonstrated. In other words, we analytically evaluate how the estimation of the categories is influenced by the estimated posterior probability, which is the output of the binary classifier, as well as by the structure of constructing the code word table.

    DOI

  • 回帰・分類問題における能動学習の研究動向と課題に関する一考察

    阪井, 優太, 小林, 学, 後藤, 正幸

    第84回全国大会講演論文集   2022 ( 1 ) 23 - 24  2022.02

     View Summary

    教師あり学習のモデルの学習において用いる教師ありデータの取得には大きなコストが発生する。そのような状況において,逐次的にモデルの精度を上げるために教師ラベルを付与するデータをサンプルすることでラベルの付与コストを抑えつつ精度の高いモデルを構築する手法として能動学習がある.能動学習は従来分類問題における議論が非常に多かったが,近年では回帰問題での研究事例も増加傾向にある.そこで本稿では,能動学習における回帰・分類問題における問題設定と最近の研究動向をまとめ紹介する.

  • 購買アイテムを特定する分類器パラメータを用いた商品分析モデルに関する一考察

    山極, 綾子, 後藤, 正幸

    第84回全国大会講演論文集   2022 ( 1 ) 5 - 6  2022.02

     View Summary

    近年ECサイト上の購買が増加し,購買履歴データから顧客の嗜好を分析し活用する企業が増えている.顧客が商品を購入する際には,嗜好に加え購入用途が影響する可能性がある.加えて,用途の影響が大きい贈答用などの商品では,その購入頻度が低いことがある.しかし従来の埋め込み手法は同一顧客が購入した商品を,その用途に関わらず類似した商品であると見なすため適用することが難しい.そういった商品群を対象とし,購買アイテムを特定する分類器パラメータを用いた商品分析モデルが提案されており,実データに対して一定の成果が得られている.本論文では,人工データを用いて上記手法が持つ特性を明らかにすることで,その有効性を示す.

  • A Proposal of Business Decision-Making Model by Bayesian Optimization Considering Input-Dependent Dispersion

    良川太河, 阪井優太, YANG Tianxiang, 後藤正幸

    情報理論とその応用シンポジウム予稿集(CD-ROM)   45th  2022

    J-GLOBAL

  • A Study on Improving the Interpretability of Biterm Topic Model by Learning of Emphasized Data Augmentation

    西田有輝, YANG Tianxiang, 山下遥, 後藤正幸

    人工知能学会全国大会論文集(Web)   36th  2022

    J-GLOBAL

  • 隠れマルコフモデルに基づくユーザのWebサイト閲覧行動分析

    高尾 洋佑, 山極 綾子, 山下 遥, 後藤 正幸

    日本計算機統計学会シンポジウム論文集   35   51 - 54  2021.11

    CiNii

  • Machine Learning for Demand Prediction of Seasonal Second-hand Fashion Items Based on Prior and Fine-tuning Prediction Models

    Fuyu Saito, Haruka Yamashita, Hokuto Sasaki, Masayuki Goto

    2021 IEEE 12th International Workshop on Computational Intelligence and Applications, IWCIA 2021 - Proceedings   IEEE IWCIA2021  2021.11  [Refereed]

    Authorship:Last author

     View Summary

    With the rapid development of information technology in recent years, it has become common for consumers to purchase various products via electric commerce (EC) sites. As a case study, this study focuses on ZOZOUSED, which is engaged in the business of buying used clothes from users, and reselling them as second-hand goods. From the perspective of inventory and management costs, it is desirable for items to be sold as soon as possible after they are listed on EC sites, and the number of listed items has been conventionally controlled, depending on the experience of item managers. However, owing to the subjective assessment of item managers, unnecessary price reductions for sales promotion of items, or opportunity losses triggered by an excessive number of listed items, may occur. For reasonable item management, the demand prediction for items by customers is a crucial task required to develop the optimal listing plan that balances supply and demand. Therefore, this study proposes a forecasting method of sales figures for the actual operation of listing second-hand goods, which comprises two-stage models: the first model is a prior seasonal long-term prediction of sales figures for each item group based on seasonal similarity, and the second model is a short-term fine-tuning prediction for daily operation via residual predictions with recent data. Furthermore, we apply the proposed model to the actual data of past sales figures accumulated in ZOZOUSED, and analyze the obtained results to demonstrate the usefulness of the proposed method. In addition, we empirically demonstrate the effectiveness of the proposed method by designing and performing an empirical experiment on an actual business by applying the output of the proposed method as a new index for determining the number of new items to be listed.

    DOI

  • Time Window Topic Model for Analyzing Customer Browsing Behavior

    Fumiyo Ito, Gendo Kumoi, Masayuki Goto

    2021 IEEE 12th International Workshop on Computational Intelligence and Applications (IEEE IWCIA2021)   IEEE IWCIA2021  2021.11  [Refereed]

    Authorship:Last author

     View Summary

    Nowadays, various services are available on the Internet, and a vast amount of website browsing history data is being accumulated. In recent years, even services with few purchase actions per user, such as booking a wedding venue or purchasing insurance, have become available on the Internet. For these services, it is assumed that the interests of users gradually change and narrow down during browsing. Then, when the users decide the product to purchase, it is considered that their interests converge on a specific subject. Therefore, it is important to implement appropriate marketing strategies depending on the degree of convergence of user interests to increase effectiveness. Therefore, a method that can analyze changing user interests over time from browsing history data is desired. In this study, we propose a Time Window Topic Model that can analyze changes in user interests by considering the interests as latent topics. The proposed method can reveal the changes in interests of users even in a real problem where it is difficult to apply conventional topic models. Finally, we verify the usefulness of the proposed method by analyzing an artificial dataset.

    DOI

  • A Study on New Product Recommendation Using Multi-Label CVAE for Fresh Flowers

    Aya Kitasato, Gendo Kumoi, Masayuki Goto

    2021 IEEE 12th International Workshop on Computational Intelligence and Applications, IWCIA 2021 - Proceedings   IEEE IWCIA2021  2021.11  [Refereed]

    Authorship:Last author

     View Summary

    In recent years, it has become very popular to use purchase history data on e-commerce sites for marketing measures to increase sales. Under such a situation, this paper considers measures using the purchase history data of a company providing delivering services of fresh flower products through an e-commerce site. This site deals mainly with fresh flowers, and the majority of items are purchased for gifts. The demands of flower gifts are usually strongly related with certain events, such as birthday, Mother's day, opening celebration, etc. Since each customer often makes purchase only at certain event when purchasing a flower gift, and it is important to encourage them to make purchases at other events from marketing viewpoint. In addition, the appearance of fresh flowers is important, so product recommendation with product images is necessary. It is relatively easy to develop floral gifts because they consist of certain patterns such as types of fresh flowers and shapes such as bouquets. However, there is no development of product which quantitatively uses purchase history information, The purpose of this research is, therefore, to generate product images that are preferred by customers in another event, considering the characteristics of product images purchased in individual event, where it is also possible to create new product images that are not contained in existing items. The proposed model is based on Conditional Variational Auto Encoader (CVAE) and can generate image outputs by inputting product images as multi-labels of events and attributes such as age and gender of customers that greatly affect product selection. Then, after learning a generator model, we consider to analyze what kinds of new products a customer with certain attributes who purchased at certain event would newly prefer at other events by changing the labels. Furthermore, in this study, we demonstrate the validity of the model by analyzing an actual data set.

    DOI

  • An Improved Translation-based Recommendation Considering Last Purchasing Sequence

    Yashu Li, Yamagiwa Ayako, Tianxiang Yang, Masayuki Goto

    2021 IEEE 12th International Workshop on Computational Intelligence and Applications (IEEE IWCIA2021)   IEEE IWCIA2021  2021.11  [Refereed]

    Authorship:Last author

    Research paper, summary (international conference)  

  • A Study on OOD Detection Based on Generative Models Trained for Each Discriminant Class

    Ryota Matsunae, Fuyu Saito, Haruka Yamashita, Masayuki Goto

    The 19th Asian Network for Quality Congress (ANQ2021)   ANQ2021 ( C10-P12-01 )  2021.10  [Refereed]

    Authorship:Last author

    Research paper, summary (international conference)  

  • An Improved Method for Estimating Conditional Average Treatment Effects Taking Account of Selection Bias Based on Causal Tree

    Yuki Tsuboi, Yuta Sakai, Satoshi Suzuki, Masayuki Goto

    The 19th Asian Network for Quality Congress (ANQ2021)   ANQ2021 ( C10-P15-02 )  2021.10  [Refereed]

    Authorship:Last author

    Research paper, summary (international conference)  

  • Store Analysis Using Latent Representation of Robust Variational Autoencoder Based on Sales History Data

    Ryogo Okubo, Ryosuke Uehara, Gendo Kumoi, Masayuki Goto, Tomohiro Yoshikai

    The 19th Asian Network for Quality Congress (ANQ2021)   ANQ2021 ( C10-P15-03 )  2021.10  [Refereed]

    Research paper, summary (international conference)  

  • A Node Sharing Learning Method for Deep Neural Networks in Multi-Label Classification

    Kodai Ishikura, Aya Kitasato, Gendo Kumoi, Masayuki Goto

    The 19th Asian Network for Quality Congress (ANQ2021)   ANQ2021 ( C10-P15-04 )  2021.10  [Refereed]

    Authorship:Last author

    Research paper, summary (international conference)  

  • A Study on Recommender System by Evaluating the Recommendation Effect of Individual Intervention

    Taichi Imafuku, Tatsuya Kawakami, Tianxiang Yang, Masayuki Goto

    The 19th Asian Network for Quality Congress (ANQ2021)   ANQ2021 ( C10-P18-03 )  2021.10  [Refereed]

    Authorship:Last author

    Research paper, summary (international conference)  

  • A Study of Diversity Analysis Model for Cooking Recipes Based on Embeddings

    Koutarou Yamashita, Fumiyo Ito, Kyosuke Hasumoto, Masayuki Goto

    The 19th Asian Network for Quality Congress (ANQ2021)   ANQ2021 ( C10-P20-03 )  2021.10  [Refereed]

    Authorship:Last author

    Research paper, summary (international conference)  

  • A Discussion on Improving Fraud Detection Performance by Generative Adversarial Networks for Transactions Data

    Guanyu Yang, Yuki Tsuboi, Ryotaro Shimizu, Gendo Kumoi, Masayuki Goto

    The 19th Asian Network for Quality Congress (ANQ2021)   ANQ2021 ( C10-P25-01 )  2021.10  [Refereed]

    Authorship:Last author

    Research paper, summary (international conference)  

  • A Study on Community Analysis Taking Account of User Preferences and Network Structure

    Linxin Song, Fuyu Saito, Haruka Yamashita, Masayuki Goto

    The 19th Asian Network for Quality Congress (ANQ2021)   ANQ2021 ( C10-P25-02 )  2021.10  [Refereed]

    Authorship:Last author

    Research paper, summary (international conference)  

  • A Study on Ensemble Learning Model with Interpretability

    Taiga Yoshikawa, Ayako Yamagiwa, Tianxiang Yang, Masayuki Goto

    The 19th Asian Network for Quality Congress (ANQ2021)   ANQ2021 ( C10-P25-03 )  2021.10  [Refereed]

    Authorship:Last author

    Research paper, summary (international conference)  

  • Predicting Customer Lifetime Value Using Neural Networks With Multi-task Learning Approach

    Kyosuke Hasumoto, Masayuki Goto

    2020 INFORMS Annual Meeting   FA61 ( AI for Decision Making III )  2020.11  [Refereed]

    Authorship:Last author

    Research paper, summary (international conference)  

  • A Study On Analysis Methods For Explaining Different Important Items Focused On Membership Stage Growth

    Tianxiang Yang, Haruka Yamashita, Masayuki Goto

    2020 INFORMS Annual Meeting   TA05 ( Big Data Analytics 1 )  2020.11  [Refereed]

    Authorship:Last author

    Research paper, summary (international conference)  

  • A Classification Model Of Consumer Living Patterns By Sparse Electricity Consumption

    Satoshi Suzuki, Masayuki Goto

    2020 INFORMS Annual Meeting   MA04 ( Data Mining 1 )  2020.11  [Refereed]

    Authorship:Last author

    Research paper, summary (international conference)  

  • An Analytical Model of Customer Purchase Factors Based on Conditional Variational Autoencoder Learned of Browsing History Data

    Tatsuya Kawakami, Yuta Sakai, Haruka Yamashita, Masayuki Goto

    18th Asian Network for Quality Congress   ANQ2020   ANQ-121  2020.10  [Refereed]

    Authorship:Last author

    Research paper, summary (international conference)  

  • A Study on Analysis Model of Customers' Purchasing Behavior based on Knowledge Graph Attention Network

    Fumiyo Ito, Zhiying Zhang, Gendo Kumoi, Masayuki Goto

    18th Asian Network for Quality Congress   ANQ2020   ANQ-125  2020.10  [Refereed]

    Authorship:Last author

    Research paper, summary (international conference)  

  • An Analytical Model of Response Interval Between Employees on Business Chat Systems Based on Latent Class Model

    Fuyu Saito, Ayako Yamagiwa, Tianxiang Yang, Masayuki Goto

    18th Asian Network for Quality Congress   ANQ2020   ANQ-130  2020.10  [Refereed]

    Authorship:Last author

    Research paper, summary (international conference)  

  • An Analytical Model of Customer Purchasing Behavior Considering Event Characteristics on Flower Delivery Business

    Aya Kitasato, Kenya Nonaka, Haruka Yamashita, Masayuki Goto

    18th Asian Network for Quality Congress   ANQ2020   ANQ-134  2020.10  [Refereed]

    Authorship:Last author

    Research paper, summary (international conference)  

  • A Prediction Model of Earned Runs Based on Latent Class Markov Chain for Starters of Professional Baseball Pitchers

    Ryosuke Uehara, Takuma Matsumoto, Kenta Mikawa, Masayuki Goto

    18th Asian Network for Quality Congress   ANQ2020 ( 0 ) ANQ-138 - 3H1GS305  2020.10  [Refereed]

    Authorship:Last author

    Research paper, summary (international conference)  

     View Summary

    <p>In recent years, quantitative analysis for baseball is performed using a large amount of accumulated data for various purposes, such as a novel defensive shift and evaluation of players. This paper proposes a predictive model of the expected runs for each inning of starting pitchers. At that time, we apply the latent class model and group the combinations of the pitcher and the batter by a small number of latent variables, and the \lq\lq batting average\rq\rq is calculated for each latent class. Consequently we construct a model to calculate expected runs considering the difference of "batting average" between pitcher and batter matches. To verify the effectiveness of the proposed method, we conduct experiments by using actual Japanese professional baseball data.</p>

    CiNii

  • Construction of Demand Forecast Model of Tokyo Taxi Based on Probe Data Analysis

    Reo Iizuka, Yuki Ono, Kenya Nonaka, Yuta Sakai, Masayuki Goto

    18th Asian Network for Quality Congress   ANQ2020   ANQ-142  2020.10  [Refereed]

    Authorship:Last author

    Research paper, summary (international conference)  

  • A Hypothesis Discovery Method for Predicting Change in Multidimensional Time-Series Data

    Gendo Kumoi, Masayuki Goto

    IEEE International Conference onf Systems, Man and Cybernetics     MoBT13.3  2020.10  [Refereed]

    Authorship:Last author

    Research paper, summary (international conference)  

    DOI

  • An Estimation Model of Life Change Point by Energy Consumption

    Satoshi Suzuki, Manabu Kobayashi, Masayuki Goto

    The 20th Asia Pacific Industrial Engineering And Management Systems (APIEMS 2019)   ( ID-309 )  2019.12  [Refereed]

    Authorship:Last author

  • An Analytical Model of Users' Communication on a Chat System

    Ayako Yamagiwa, Yuto Seko, Tianxiang Yang, Masayuki Goto

    The 20th Asia Pacific Industrial Engineering And Management Systems (APIEMS 2019)   ( ID-208 )  2019.12  [Refereed]

    Authorship:Last author

    Research paper, summary (international conference)  

  • Predicting Customer Churn of a Platform Business Using Latent Variables of Variational Autoencoder and Analysis of Customers’ Purchasing Behaviors

    Kyosuke Hasumoto, Masayuki Goto

    The 20th Asia Pacific Industrial Engineering And Management Systems (APIEMS 2019)   APIEMS2019 ( ID-94 )  2019.12  [Refereed]

    Authorship:Last author

  • Estimation Problem of Linear Regression with High-Dimensional Sparse Supplementary Variables

    Masayuki Goto

    The 20th Asia Pacific Industrial Engineering And Management Systems (APIEMS 2019)   ( ID-210 )  2019.12  [Refereed]

    Authorship:Lead author, Last author, Corresponding author

  • A Study on Recommender System Considering Diversity of Items Based on LDA

    Zhiying Zhang, Taiju Hosaka, Haruka Yamashita, Masayuki Goto

    2019 Asian Conference of Management Science & Applications (ACMSA2019)   1  2019.10  [Refereed]

    Authorship:Last author

    Research paper, summary (international conference)  

  • A QA System Model Considering Relationship of Topics between Question/Answer Documents

    Junya Okawa, Gendo Kumoi, Masayuki Goto

    2019 Asian Conference of Management Science & Applications (ACMSA2019)    2019.10  [Refereed]

    Authorship:Last author

    Research paper, summary (international conference)  

  • A Study on Accuracy of Causal Effects Estimation for Unknown True Causal Structure

    Kazuma Inoue, Gendo Kumoi, Shunsuke Horii, Tota Suko, Masayuki Goto

    2019 Asian Conference of Management Science & Applications (ACMSA2019)    2019.10  [Refereed]

    Authorship:Last author

    Research paper, summary (international conference)  

  • An Analytical Model of Web Sites Relationship Based on Browsing History Embedding Considering Page Transitions

    Taiju Hosaka, Haruka Yamashita, Masayuki Goto

    2019 Asian Conference of Management Science & Applications (ACMSA2019)    2019.10  [Refereed]

    Authorship:Last author

    Research paper, summary (international conference)  

  • An Analytical Model of Exhibition Price Change Effects on Second-Hand Fashion EC Site

    Shimpei Kanazawa, Tianxiang Yang, Masayuki Goto

    2019 Asian Conference of Management Science & Applications (ACMSA2019)    2019.10  [Refereed]

    Authorship:Last author

    Research paper, summary (international conference)  

  • Study on Item Recommendation Considering Store Information in Cyber Mall

    Yuichi Ohhori, Masayuki Goto, Haruka Yamashita

    2019 Asian Conference of Management Science & Applications (ACMSA2019)    2019.10  [Refereed]

    Research paper, summary (international conference)  

  • Important Variables Selection for Customer Feature Analysis Using Anomaly Detection Method

    Hirotake Arai, Gendo Kumoi, Masayuki Goto

    2019 Asian Conference of Management Science & Applications (ACMSA2019)    2019.10  [Refereed]

    Authorship:Last author

    Research paper, summary (international conference)  

  • Factorization Machines Considering the Latent Characteristics Behind Target Data

    Tomoya Sugisaki, Kenta Mikawa, Masayuki Goto

    2019 Asian Conference of Management Science & Applications (ACMSA2019)    2019.10  [Refereed]

    Authorship:Last author

    Research paper, summary (international conference)  

  • A Study of Out of Manifold Data Augmentation in Deep Neural Network

    Hideki Fujinami, Gendo Kumoi, Masayuki Goto

    2019 Asian Conference of Management Science & Applications (ACMSA2019)    2019.10  [Refereed]

    Authorship:Last author

    Research paper, summary (international conference)  

  • A Study on Customer Purchase Behavior Analysis Based on Hidden Topic Markov Models

    Mio Hotoda, Gendo Kumoi, Masayuki Goto

    2019 Asian Conference of Management Science & Applications (ACMSA2019)    2019.10  [Refereed]

    Authorship:Last author

    Research paper, summary (international conference)  

  • An Analytical Model to Activate Non-Royal Users of Multifunctional Credit Card

    Yuto Seko, Gendo Kumoi, Masayuki Goto, Tetsuya Tachibana

    2019 Asian Conference of Management Science & Applications (ACMSA2019)    2019.10  [Refereed]

    Research paper, summary (international conference)  

  • An Analytical Model of Consumers Purchasing Behavior Considering the Variety of Products

    Kazuki Yasui, Kenta Mikawa, Masayuki Goto

    2019 Asian Conference of Management Science & Applications (ACMSA2019)    2019.10  [Refereed]

    Authorship:Last author

  • An Analysis Method Based on Customer Definition for Detecting Latent Customer Purchase Behavior Focused on Membership Stage Growth

    Tianxiang Yang, Haruka Yamashita, Masayuki Goto

    2019 Asian Conference of Management Science & Applications (ACMSA2019)    2019.10  [Refereed]

    Authorship:Last author

    Research paper, summary (international conference)  

  • Multi-Valued Classification Based on ECOC with Support Vector Machine

    Ayako, Yamagiwa, Haruka Yamashita, Masayuki, Goto

    17th Asian Network for Quality Congress   ANQ2019 ( ID-225 )  2019.10  [Refereed]

    Authorship:Last author

  • A Study on Text Analysis Model to Support Creation of Summarized Weather Forecast

    Takuma Matsumoto, Yuto Seko, Gendo Kumoi, Masayuki Goto, Tomohiro Yoshikai

    17th Asian Network for Quality Congress   ANQ2019 ( ID-218 )  2019.10  [Refereed]

  • Collaborative Filtering Based on Distributed Expression Considering Difference in Evaluation Tendencies

    Ryousuke Goto, Hideki Fujinami, Tianxiang Yang, Masayuki Goto

    17th Asian Network for Quality Congress   ANQ2019 ( ID-223 )  2019.10  [Refereed]

    Authorship:Last author

  • An Extension of Semi-Supervised Boosting to Multiclass Classification

    Yuta Sakai, Kazuki Yasui, Kenta Mikawa, Masayuki Goto

    17th Asian Network for Quality Congress   ANQ2019 ( ID-180 )  2019.10  [Refereed]

    Authorship:Last author

  • An Estimation Model of Open Price for Second-hand Fashion Items Based on Sales History Data

    Izumi Kuwata, Tomoya Sugisaki, Kenta Mikawa, Masayuki Goto

    17th Asian Network for Quality Congress   ANQ2019 ( ID-179 )  2019.10  [Refereed]

  • A Study on Analysis Methods of Latent Customer Purchase Behavior Focused on Membership Stage Growth

    Tianxiang Yang, Haruka Yamashita, Masayuki Goto

    2019 IEEE International Conference on Big Data, Cloud Computing, Data Science & Engineering (BCD 2019)   BCD 2019  2019.05  [Refereed]

    Authorship:Last author

    Research paper, summary (international conference)  

    DOI

  • Greedy Features Quantity Selection Method from Multivariate Time Series Data for Customer Classification

    Gendo Kumoi, Masayuki Goto

    2019 IEEE International Conference on Big Data, Cloud Computing, Data Science & Engineering (BCD 2019)   BCD 2019  2019.05  [Refereed]

    Authorship:Last author

    Research paper, summary (international conference)  

    DOI

  • Latent Class Models on Business Analytics

    Masayuki Goto

    2019 IEEE International Conference on Big Data, Cloud Computing, Data Science & Engineering (BCD 2019)   BCD 2019  2019.05  [Refereed]

    Authorship:Lead author, Last author, Corresponding author

    Research paper, summary (international conference)  

    DOI

  • 真の因果構造が未知の場合の因果効果の推定精度について

    井上一磨, 雲居玄道, 堀井俊佑, 須子統太, 後藤正幸

    情報理論とその応用シンポジウム予稿集(CD-ROM)   42nd  2019

    J-GLOBAL

  • 閲覧の遷移行動を考慮した分散表現に基づくWebサイトの関係分析モデル

    保坂大樹, 山下遥, 後藤正幸

    情報理論とその応用シンポジウム予稿集(CD-ROM)   42nd  2019

    J-GLOBAL

  • EM-NMF ensemble method by weighted least squares method considering the number of evaluations

    大堀祐一, 山下遥, 後藤正幸

    人工知能学会全国大会論文集(Web)   33rd  2019

    J-GLOBAL

  • A study on recommender system considering diversity in recommendation items based on LDA

    ZHANG Zhiying, 保坂大樹, 山下遥, 後藤正幸

    人工知能学会全国大会論文集(Web)   33rd  2019

    J-GLOBAL

  • Development of problem extraction tool for debugging practice using learning history

    Katsuyuki Umezawa, Makoto Nakazawa, Masayuki Goto, Shigeichi Hirasawa

    The 17th Annual Hawaii International Conference on Education    2019.01  [Refereed]

  • A Pattern Analysis Model Based on Users' Action Series on a Portal Site for Job-hunting

    Yuuki Sugiyama, Gendo Kumoi, Masayuki Goto, Takashi Sakurai

    The 19th Asia Pacific Industrial Engineering and Management Systems   APIEMS2018  2018.12  [Refereed]

  • Consumer Purchasing Behavior Analysis Model for Purchase History Data Stored in Credit Card

    Ryotaro Shimizu, Haruka Yamashita, Masayuki Goto, Ranna Tanaka

    The 19th Asia Pacific Industrial Engineering and Management Systems   APIEMS2018  2018.12  [Refereed]

  • Proposal for an l1 regularized Factorization Machine

    Kenta Mikawa, Manabu Kobayashi, Masayuki Goto, Shigeichi Hirasawa

    The 19th Asia Pacific Industrial Engineering and Management Systems   APIEMS2018  2018.12  [Refereed]

  • An Analysis of Web Access Log Data Based on Graph Mining Method

    Ryota Kawabe, Haruka Yamashita, Masayuki Goto

    The 19th Asia Pacific Industrial Engineering and Management Systems   APIEMS2018  2018.12  [Refereed]

    Authorship:Last author

  • A Study of the Application of Canonical Correlation Forests to Text Classification

    Shuhei Nakano, Kenta Mikawa, Masayuki Goto

    The 19th Asia Pacific Industrial Engineering and Management Systems   APIEMS2018  2018.12  [Refereed]

    Authorship:Last author

  • Analysis of Consumer Panel Data Based on Discriminant Non-negative Matrix Factorization

    Tsubasa Ano, Haruka Yamashita, Masayuki Goto

    The 19th Asia Pacific Industrial Engineering and Management Systems   APIEMS2018  2018.12  [Refereed]

    Authorship:Last author

  • A Model to Detect Unique Customers Based on Time-series Log Data

    Xiaoyan Zhang, Haruka Yamashita, Gendo Kumoi, Masayuki Goto

    The 19th Asia Pacific Industrial Engineering and Management Systems   APIEMS2018  2018.12  [Refereed]

    Authorship:Last author

  • An Analytical Model for Customer Purchasing Behavior Based on a Generative Model of RFM Measures

    Yuri Nishio, Haruka Yamashita, Masayuki Goto

    The 19th Asia Pacific Industrial Engineering and Management Systems   APIEMS2018  2018.12  [Refereed]

    Authorship:Last author

  • A Study on Automatic Classification of Query Documents Considering Question Contents

    Keisuke Okubo, Gendo Kumoi, Masayuki Goto

    The 19th Asia Pacific Industrial Engineering and Management Systems   APIEMS2018  2018.12  [Refereed]

    Authorship:Last author

  • An Analytical Model of Customer Purchase Behavior Considering Page Transitions on EC Site

    Mio Hotoda, Hiroki Mizuochi, Gendo Kumoi, Masayuki Goto

    16th Asian Network for Quality Congress   ANQ2018 ( JP-042 )  2018.09  [Refereed]

    Authorship:Last author

  • A Study on Feature Clustering Analysis by the Hidden Layer of Autoencoder

    Shimpei KANAZAWA, Yuuki SUGIYAMA, Tianxiang YANG, Masayuki GOTO

    16th Asian Network for Quality Congress   ANQ2018 ( JP-044 )  2018.09  [Refereed]

    Authorship:Last author

  • A Prediction Model of Item Demands Based on Temperature Gap and Store Characteristics

    Yuto SEKO, Ryotaro SHIMIZU, Gendo KUMOI, Masayuki GOTO, Tomohiro YOSHIKAI

    16th Asian Network for Quality Congress   ANQ2018  2018.09  [Refereed]

  • A New Entry Behavior Model of Student Users on Job Board for New Graduates Considering the Interaction between Features

    Tomoya SUGISAKI, Yuri NISHIO, Kenta MIKAWA, Masayuki GOTO, Takashi SAKURAI

    16th Asian Network for Quality Congress   ANQ2018 ( JP-045 )  2018.09  [Refereed]

  • A Visualization System of the Contribution of Learners in Software Development PBL Using GitHub

    Yutsuki Miyashita, Atsuo Hazeyama, Hiroaki Hashiura, Masayuki Goto, Shigeichi Hirasawa

    Proceedings - Asia-Pacific Software Engineering Conference, APSEC   2018-December   695 - 696  2018.07  [Refereed]

     View Summary

    In recent years, the paradigm of social coding in software development has attracted attention to developers all over the world, and GitHub which is a social coding tool has spread to the area like education. There are many cases using it as a platform of PBL (Project Based Learning). However, since GitHub is not a tool for education, it is difficult to evaluate learners. This research focuses on the contribution of learners and proposes a system that teachers can grasp the contribution of learners.

    DOI

  • A Greedy Construction Approach of Codeword Table on Error Correcting Output Coding for Multivalued Classification and Its Evaluation by Using Artificial Data

    Gendo Kumoi, Hideki Yagi, Manabu Kobayashi, Masayuki Goto, Shigeichi Hirasawa

    2018 International Conference of Engineering, Technology, and Applied Science   ICETA 2018   15 - 22  2018.06  [Refereed]

  • System Evaluation of Error Correcting Output Codes for Artificial Data Methods

    Shigeichi Hirasawa, Gendo Kumoi, Manabu Kobayashi, Masayuki Goto, Hiroshige Inazumi

    2018 International Conference of Engineering, Technology, and Applied Science   ICETA 2018   112 - 122  2018.06  [Refereed]

  • System evaluation of construction methods for multi-class problems using binary classifiers

    Shigeichi Hirasawa, Gendo Kumoi, Manabu Kobayashi, Masayuki Goto, Hiroshige Inazumi

    Advances in Intelligent Systems and Computing   746   909 - 919  2018  [Refereed]

     View Summary

    Construction methods for multi-valued classification (multi-class) systems using binary classifiers are discussed and evaluated by a trade-off model for system evaluation based on rate-distortion theory. Suppose the multi-class systems consisted of M(≥3) categories and N(≥M-1) binary classifiers, then they can be represented by a matrix W, where the matrix W is given by a table of M code words with length N, called a code word table. For a document classification task, the relationship between the probability of classification error Pe and the number of binary classifiers N for given M is investigated, and we show that our constructed systems satisfy desirable properties such as “Flexible”, and “Elastic”. In particular, modified Reed Muller codes perform well: they are shown to be “Effective elastic”. As a second application we consider a hand-written character recognition task, and we show that the desirable properties are also satisfied.

    DOI

  • A Study on Prediction Model of Selling Prices of Second-hand Fashion Items

    Masato Ninohira, Kenta Mikawa, Masayuki Goto

    The 18th Asia Pacific Industrial Engineering and Management System Conference   APIEMS2017 ( ID158 )  2017.12  [Refereed]

    Authorship:Last author

  • A Study on Extraction of Important Items Focused on Customer Growth Based on Network Analysis

    Hiroaki Ito, Gendo Kumoi, Masayuki Goto

    The 18th Asia Pacific Industrial Engineering and Management System Conference   APIEMS2017 ( ID164 )  2017.12  [Refereed]

    Authorship:Last author

  • Characteristics of a Word Segmentation Method Based on a State-transition Model

    Makoto Suzuki, Naohide Yamagishi, Kenta Mikawa, Masayuki Goto

    The 18th Asia Pacific Industrial Engineering and Management System Conference   APIEMS2017 ( ID158 )  2017.12  [Refereed]

    Authorship:Last author

  • The Bayesian Prediction Algorithm Using Logistic Regression

    Takumi Arai, Kenta Mikawa, Masayuki Goto

    The 18th Asia Pacific Industrial Engineering and Management System Conference   APIEMS2017 ( ID164 )  2017.12  [Refereed]

    Authorship:Last author

  • A Model for Relational Analysis of Recommendation Articles and Reactions on Gourmet Service Site

    Teppei Sakamoto, Haruka Yamashita, Masayuki Goto, Jiro Iwanaga

    The 18th Asia Pacific Industrial Engineering and Management System Conference   APIEMS2017 ( ID158 )  2017.12  [Refereed]

  • Binary Document Classification Based on Fast Flux Discriminant with Similarity Measure on Word Set

    Keisuke Okubo, Gendo Kumoi, Masayuki Goto

    2017 Asian Conference of Management Science & Applications   ACMSA 2017  2017.12  [Refereed]

    Authorship:Last author

  • A Recommendation System Based on NMF Approach for Solving Cold Start Problem

    Xiaoyan Zhang, Sei Okayama, Haruka Yamashita, Masayuki Goto

    2017 Asian Conference of Management Science & Applications   ACMSA 2017  2017.12  [Refereed]

    Authorship:Last author

  • A Study on Analytical Method of Printer Data Log for Detecting Excellent Customers

    Shuhei Nakano, Kenta Mikawa, Masayuki Goto

    2017 Asian Conference of Management Science & Applications   ACMSA 2017  2017.12  [Refereed]

    Authorship:Last author

  • Transfer Learning Based on Probabilistic Latent Semantic Analysis for Analyzing Purchase Behavior Considering Customers' Membership Stages

    Tianxiang Yang, Gendo Kumoi, Haruka Yamashita, Masayuki Goto

    2017 Asian Conference of Management Science & Applications   ACMSA 2017  2017.12  [Refereed]

    Authorship:Last author

  • A Study on Semi-supervised Learning Using a Small Number of Positive Example Documents and Unlabeled Documents

    Hiroki Mizuochi, Sei Okayama, Gendo Kumoi, Masayuki Goto

    15th Asian Network for Quality Congress   ANQ2017  2017.09  [Refereed]

  • A New Analytical Model for Customer Growth Considering Potential Purchasing Preferences

    Yuri Nishio, Hiroaki Itou, Haruka Yamashita, Masayuki Goto

    15th Asian Network for Quality Congress   ANQ2017  2017.09  [Refereed]

  • Proposal of Hierarchical Structure Learning of Bayesian Network for Analyzing Customer Purchasing Behavior

    Ryota Kawabe, Hiroaki Itou, Haruka Yamashita, Masayuki Goto

    15th Asian Network for Quality Congress   ANQ2017  2017.09  [Refereed]

  • Proposal of a purchase behavior analysis model on EC site considering questionnaire data

    Ryotaro Shimizu, Teppei Sakamoto, Haruka Yamashita, Masayuki Goto

    15th Asian Network for Quality Congress   ANQ2017  2017.09  [Refereed]

  • An Analytical Model of Relation Between Browsing and Entry Activities on an Internet Portal Site for Job-hunting

    Yuuki Sugiyama, Takumi Arai, Tianxiang Yang, Masayuki Goto, Tairiku Ogihara

    15th Asian Network for Quality Congress   ANQ2017  2017.09  [Refereed]

  • A Study on Ensemble Learning Focusing on Local Structure

    Daiki Gyoten, Masato Ninohira, Kenta Mikawa, Masayuki Goto

    15th Asian Network for Quality Congress   ANQ2017  2017.09  [Refereed]

  • An Investigation into the Awareness of the Need for Quality Education in Nepal

    Megumi Asada, Haruka Yamashita, Manita Shrestha, Masayuki Goto, Brenda Bushell

    Canadian Student Research Conference 2017   CSRC2017  2017.07  [Refereed]

  • A Study of the Present and Future Utilization of ICT in Nepal

    Hirotake Arai, Haruka Yamashita, Manita Shrestha, Masayuki Goto, Brenda Bushell

    Canadian Student Research Conference 2017   CSRC2017  2017.07  [Refereed]

  • Awareness and Opinions of Nepalese Citizens on Gender Inequality in Nepal

    Hikari Nakamura, Brenda Bushell, Masayuki Goto

    Canadian Student Research Conference 2017   CSRC2017  2017.07  [Refereed]

  • 顧客の成長に着目したネットワーク分析による重要商品の抽出に関する一考察

    伊藤, 寛彬, 雲居, 玄道, 山下, 遥, 後藤, 正幸

    第79回全国大会講演論文集   2017 ( 1 ) 271 - 272  2017.03

     View Summary

    近年,データベースやインターネットテクノロジーの進化により顧客がいつ,どこで,何を買ったのかについての詳細なデータを蓄積することが可能となった.このようなデータを活用した解析の中には,優良顧客をどのように獲得するのか,どのような施策を講じれば,離反顧客になることを抑止できるのか,といった様々な観点からの解析が存在する.本研究では,某小売店の購買履歴データを分析対象とし,顧客ごとに存在する会員ステージに着目する.非優良顧客と優良顧客にはどのような購買傾向の違いがあり,顧客を成長させるために重要度が高い商品は何か,をクラスタ分析及び重要度分析から明らかにすることで,顧客の成長への示唆が与えられる.

    CiNii

  • Discrimination Model for Synthetic Variables Generated from Explanatory Variables Considering Sample Attributes

    Haruka Yamashita, Masayuki Goto

    The 17th Asia Pacific Industrial Engineering and Management Systems Conference   APIEMS 2016  2016.10  [Refereed]

  • Word Acquisition of Japanese Classical Literature Using State Transition Model

    Makoto Suzuki, Bin Xu, Naohide Yamagishi, Masayuki Goto

    The 17th Asia Pacific Industrial Engineering and Management Systems Conference   APIEMS 2015  2016.10  [Refereed]

  • Modeling customer purchase behavior based on page transitions by latent class model for customer segmentation

    Yuki Matsuzaki, Kenta Mikawa, Masayuki Goto

    The 17th Asia Pacific Industrial Engineering and Management Systems Conference   APIEMS 2014  2016.10  [Refereed]

  • A coupon effect model considering behavior data

    Kaitaro Endo, Haruka Yamashita, Masayuki Goto

    The 17th Asia Pacific Industrial Engineering and Management Systems Conference   APIEMS 2014  2016.10  [Refereed]

  • A proposal of document recommendation based on topic model

    Yusei Yamamoto, Kenta Mikawa, Masayuki Goto

    The 17th Asia Pacific Industrial Engineering and Management Systems Conference   APIEMS 2014  2016.10  [Refereed]

  • Multi-Category Classification Based on ECOC Approach Using Sub-categories

    Leona Suzuki, Haruka Yamashita, Masayuki Goto

    The 17th Asia Pacific Industrial Engineering and Management Systems Conference   APIEMS 2014  2016.10  [Refereed]

  • An Analytic Model of Relation between Companies' Recruitment Activities and Number of Students' Application Based on Mixture Regression Model

    Seiya Nagamori, Haruka Yamashita, Masayuki Goto, Tairiku Ogihara

    The 17th Asia Pacific Industrial Engineering and Management Systems Conference   APIEMS 2014  2016.10  [Refereed]

  • A Study on Distance Metric Learning using Distance Structure among Category Centroids

    Kenta Mikawa, Manabu Kobayashi, Masayuki Goto, Shigeichi Hirasawa

    The 17th Asia Pacific Industrial Engineering and Management Systems Conference   APIEMS 2014  2016.10  [Refereed]

  • A Latent Class Model to Analyze the Relationship Between Companies’ Appeal Points and Students’ Reasons for Application

    Teppei Sakamoto, Haruka Yamashita, Masayuki Goto, Tairiku Ogihara

    The 7th Forum for Council of Industrial Engineering and Logistics Management Department Heads & The 5th Institute of Industrial and Systems Engineering Asian Conference   CIEDH2016, IISEAsia2016  2016.07  [Refereed]

  • Customer clustering based on latent class model representing preference for item seasonality

    Masato Ninohira, Leona Suzuki, Haruka Yamashita, Masayuki Goto

    The 7th Forum for Council of Industrial Engineering and Logistics Management Department Heads & The 5th Institute of Industrial and Systems Engineering Asian Conference   CIEDH2016, IISEAsia2015  2016.07  [Refereed]

  • A study of Improving Classification Accuracy of k-nearest Neighbor Based on Local Metric Learning and Adaptive Weighted Ensemble

    Shuhei Nakano, Seiya Nagamori, Kenta Mikawa, Masayuki Goto

    The 7th Forum for Council of Industrial Engineering and Logistics Management Department Heads & The 5th Institute of Industrial and Systems Engineering Asian Conference   CIEDH2016, IISEAsia2014  2016.07  [Refereed]

  • An Approximate Bayesian Prediction Algorithm Based on Ensemble Learning

    Takumi Arai, Yusei Yamamoto, Kenta Mikawa, Masayuki Goto

    The 7th Forum for Council of Industrial Engineering and Logistics Management Department Heads & The 5th Institute of Industrial and Systems Engineering Asian Conference   CIEDH2016, IISEAsia2013  2016.07  [Refereed]

  • Educating for Sustainable Development in Nepal: Findings from an Environmental Awareness and Disaster Prevention School Program

    Satomi Ito, Masayuki Goto, Masaaki Sugihara

    22nd International Interdisciplinary Conference on the Environment   IICE 2016  2016.06  [Refereed]

  • A Survey to Identify the Necessary Support and Appropriate Disaster Risk Management for Nepal

    Yuri Nishio, Masayuki Goto, Brenda Bushell, Manita Shrestha

    22nd International Interdisciplinary Conference on the Environment   IICE 2016  2016.06  [Refereed]

  • Research on the Political Crisis and its Impact on the Economic and Social Sectors in Nepal

    Ryotaro Shimizu, Haruka Yamashita, Masayuki Goto, Brenda Bushell

    22nd International Interdisciplinary Conference on the Environment   IICE 2016  2016.06  [Refereed]

  • Latent Class Model Analysis Based on the Variational Bayes

    Manabu Kobayashi, Masayuki Goto, Toshiyasu Matsushima, Shigeichi Hirasawa

    2016 RISP International Workshop on Nonlinear Circuits, Communications and Signal Processing   NCSP'16  2016.03  [Refereed]

  • Multi-valued Classification of Text Data based on ECOC Approach using Ternary Orthogonal Table

    Leona Suzuki, Kan Yamagami, Kenta Mikawa, Masayuki Goto

    The 16th Asia Pacific Industrial Engineering and Management Systems Conference (APIEMS 2015),   APIEMS 2015  2015.12  [Refereed]

  • Analysis of Customer Purchase Behavior by using Purchase History with Discount Coupon Based on Latent Class Model

    Yuki Matsuzaki, Kan Yamagami, Kenta Mikawa, Masayuki Goto

    The 16th Asia Pacific Industrial Engineering and Management Systems Conference (APIEMS 2015),   APIEMS 2015  2015.12  [Refereed]

  • A Finish Date Prediction of Job Hunting based on User Clustering Approach considering Time Series Variation of Entry Tendencies

    Seiya Nagamori, Kenta Mikawa, Masayuki Goto, Tairiku Ogihara

    The 16th Asia Pacific Industrial Engineering and Management Systems Conference (APIEMS 2015),   APIEMS 2015  2015.12  [Refereed]

  • A Proposal for Classification of Document Data with Unobserved Categories Considering Latent Topics

    Yusei Yamamoto, Kenta Mikawa, Masayuki Goto

    The 16th Asia Pacific Industrial Engineering and Management Systems Conference (APIEMS 2015),   APIEMS 2015  2015.12  [Refereed]

  • Adaptive Prediction Method Based on Alternating Decision Forests Considering Generalization Ability

    Shotaro Misawa, Kenta Mikawa, Masayuki Goto

    The 16th Asia Pacific Industrial Engineering and Management Systems Conference (APIEMS 2015),   APIEMS 2015  2015.12  [Refereed]

  • A Statistical Prediction Model of Students' Finishing Date on Job Hunting Using Internet Portal Sites Data

    Kan Yamagami, Kenta Mikawa, Masayuki Goto, Tairiku Ogihara

    The 16th Asia Pacific Industrial Engineering and Management Systems Conference (APIEMS 2015),   APIEMS 2015  2015.12  [Refereed]

  • Language-independent Word Acquisition Method Using State Transition Model

    Bin Xu, Makoto Suzuki, Naohide Yamagishi, Masayuki Goto

    The 16th Asia Pacific Industrial Engineering and Management Systems Conference (APIEMS 2015),   APIEMS 2015  2015.12  [Refereed]

  • Analysis of Purchase History Data Based on a New Latent Class Model for RFM Analysis

    Zhang Qian, Haruka Yamashita, Kenta Mikawa, Masayuki Goto

    2015 Asian Conference of Management Science & Applications   ACMSA2015  2015.09  [Refereed]

  • The Study of Distributed Support Vector Machine with Lower Time Computational Complexity

    Kiichiro Yukawa, Kenta Mikawa, Masayuki Goto

    2015 Asian Conference of Management Science & Applications   ACMSA2015  2015.09  [Refereed]

  • A Bayes Prediction Algorithm for the Model Class Conditioned by the Cumulative Number of Event Occurrences

    Hiromu Auchi, Kenta Mikawa, Masayuki Goto

    2015 Asian Conference of Management Science & Applications   ACMSA2015  2015.09  [Refereed]

  • A Proposal of Classification Method Based on Local Metric Matrices

    Hiroshi Saito, Kenta Mikawa, Masayuki Goto

    2015 Asian Conference of Management Science & Applications   ACMSA2015  2015.09  [Refereed]

  • Data Pair Selection for Improving Classification Accuracy of Information-Theoretic Metric Learning

    Takashi Maga, Kiichiro Yukawa, Kenta Mikawa, Masayuki Goto

    2015 Asian Conference of Management Science & Applications   ACMSA2015  2015.09  [Refereed]

  • An Analysis Based on Principal Matrix Decomposition for 3-mode Binary Data

    Haruka Yamashita, Masayuki Goto

    2015 Asian Conference of Management Science & Applications   ACMSA2015  2015.09  [Refereed]

  • An Analysis of Purchasing and Browsing Histories on an EC Site Based on a New Latent Class Mod

    Masayuki Goto, Kenta Mikawa, Manabu Kobayashi, Shunsuke Horii, Tota Suko, Shigeichi Hirasawa

    The 1st East Asia Workshop on Industrial Engineering   EAWIE 2014  2014.11  [Refereed]

  • A New Estimation Method of Latent Class Model with High Accuracy by Using Both Browsing and Purchase Histories

    Naohiro Fujiwara, Kenta Mikawa, Masayuki Goto

    The 15th Asia Pacific Industrial Engineering and Management Systems Conference (APIEMS 2014)   APIEMS2014  2014.10  [Refereed]

  • A Statistical Model for Recommender System to Maximize Sales Amount Focusing on Characteristics of EC Site Data

    Kan Yamagami, Naohiro Fujiwara, Kenta Mikawa, Masayuki Goto

    The 15th Asia Pacific Industrial Engineering and Management Systems Conference (APIEMS 2014)   APIEMS2014  2014.10  [Refereed]

  • Distance Metric Learning with Low Computational Complexity based on Ensemble of Low-dimensional Matrices

    Hiroshi Saito, Fumihiro Yamazaki, Kenta Mikawa, Masayuki Goto

    The 15th Asia Pacific Industrial Engineering and Management Systems Conference (APIEMS 2014)   APIEMS2014  2014.10  [Refereed]

  • The Proposal of Statistical Model Selection of Linear Regression for Privacy Preserving Data Mining

    Kiichiro Yukawa, Kenta Mikawa, Masayuki Goto

    The 15th Asia Pacific Industrial Engineering and Management Systems Conference (APIEMS 2014)   APIEMS2014  2014.10  [Refereed]

  • A Prediction Method based on Weighted Ensemble of Decision Tree on Alternating Decision Forests

    Shotaro Misawa, Naohiro Fujiwara, Kenta Mikawa, Masayuki Goto

    The 15th Asia Pacific Industrial Engineering and Management Systems Conference (APIEMS 2014)   APIEMS2014  2014.10  [Refereed]

  • A Comparative Survey on Sustainable Tourism Development Considering Regional Characteristics in Nepal

    Yuki Matsuzaki, Seiya Nagamori, Kenta Mikawa, Masayuki Goto, Brenda Bushell

    20th International Interdisciplinary Conference on the Environmental, Denver, Colorado, USA   IICE2014  2014.06  [Refereed]

  • Community Attitude, Willingness and Responsibility towards Waste Management in Kathmandu City, Nepal

    Mako Hidaka, Eri Shigeta, Brenda Bushell, Masayuki Goto

    20th International Interdisciplinary Conference on the Environmental, Denver, Colorado, USA   IICE2013  2014.06  [Refereed]

  • 詳細な学習履歴を活用した学習者行動の分析

    中澤真, 小泉大城, 後藤正幸, 平澤茂一

    第76回全国大会講演論文集   2014 ( 1 ) 357 - 359  2014.03

     View Summary

    ICTの進歩とともに,学習者は教材の閲覧からテスト,ノートテイキング,コミュニケーションに至るまですべての学習活動をe-learningのシステム上で行えるようになりつつある.これは学習者の活動履歴も取得しやすくなることを意味し,結果として,これらの情報を用いた学習者の進捗に応じた個別学習支援や,問題のある学生を自動抽出して教員へフィードバックする授業支援機能などの実現が期待されている.しかし,単純なアクセス回数やファイルの閲覧回数だけでは学習者の状態を正確に同定することは難しい.そこで本稿では,講義資料のページ単位の閲覧や練習問題の取り組み状況などの詳細な学習履歴を活用し,学習者の行動特性を分析し,これを明らかにする.

    CiNii

  • A Study on Recommender System Based on Latent Class Model for High Dimensional and Sparse Data

    Shunsuke Sakamoto, Kenta Mikawa, Masayuki Goto

    The 14th Asia Pacific Industrial Engineering and Management Systems Conference (APIEMS 2013),   APIEMS2013  2013.12  [Refereed]

  • Training Data Selection in Large Margin Nearest Neighbor Method for Classification Problems

    Fumihiro Yamazaki, Shunsuke Sakamoto, Kenta Mikawa, Masayuki Goto

    The 14th Asia Pacific Industrial Engineering and Management Systems Conference (APIEMS 2013)   APIEMS2013  2013.12  [Refereed]

  • Multi-valued Classification of Text Data Based on ECOC Approach Considering Parallel Processing

    Tairiku Ogihara, Kenta Mikawa, Masayuki Goto

    The 14th Asia Pacific Industrial Engineering and Management Systems Conference (APIEMS 2013)   APIEMS2013  2013.12  [Refereed]

  • A Statistical Prediction Model of Students' Success on Job Hunting by Log Data

    Mao Hayakawa, Kenta Mikawa, Takashi Ishida, Masayuki Goto

    The 14th Asia Pacific Industrial Engineering and Management Systems Conference (APIEMS 2012)   APIEMS2013  2013.12  [Refereed]

    Authorship:Last author

    Research paper, summary (international conference)  

  • A Study of Recommender Systems Based on the Latent Class Model Estimated by Combining Both Evaluation and Purchase Histories

    Takahiro Oi, Kenta Mikawa, Masayuki Goto

    The 14th Asia Pacific Industrial Engineering and Management Systems Conference (APIEMS 2012)   APIEMS2013  2013.12  [Refereed]

  • Regularized Distance Metric Learning and its Application to Knowledge Discovery

    Kenta Mikawa, Takashi Ishida, Masayuki Goto

    The 14th Asia Pacific Industrial Engineering and Management Systems Conference (APIEMS 2012)   APIEMS2013  2013.12  [Refereed]

  • Language-independent Text Categorization by Word N-gram Using an Automatic Acquisition of Words

    Makoto Suzuki, Naohide Yamagishi, Yi-Ching Tsai, Masayuki Goto

    The 14th Asia Pacific Industrial Engineering and Management Systems Conference (APIEMS 2012)   APIEMS2013  2013.12  [Refereed]

  • The Survey for Sustainable Tourism Development

    Naohiro Fujiwara, Yo Nishihara, Masayuki Goto, Brenda Bushell

    19th International Interdisciplinary Environmental Conference, Portland, Oregon   IICE2013  2013.06  [Refereed]

  • Rate-Compatible Punctured Low-Density Parity-Check Codes Consisting of Two Subgraphs

    Gou Hosoya, Keishi Osada, Masayuki Goto

    2013 International Workshop on Nonlinear Circuits, Communications and Signal Processing, NCSP'13   NCSP'13   181 - 184  2013.03  [Refereed]

  • A Proposal of Adaptive Metric Learning to Each Category Characteristics for Text Classification

    Kenta Mikawa, Takashi Ishida, Masayuki Goto, Shigeichi Hirasawa

    2013 International Workshop on Nonlinear Circuits, Communications and Signal Processing, NCSP'13   NCSP'13   544 - 547  2013.03  [Refereed]

  • The Agent-Based Simulation Analysis of Collaborative Filtering Using Mixed Membership Stochastic Block Models

    Yusuke Izawa, Kenta Mikawa, Masayuki Goto

    The 13th Asia Pacific Industrial Engineering and Management Systems Conference (APIEMS 2012)   APIEMS 2012 ( No.146-1 )  2012.12  [Refereed]

  • Multi-valused Document Classification based on Generalized Bradley-Terry Classifiers Utilizing Accuracy Information

    Tairiku Ogihara, Kenta Mikawa, Gou Hosoya, Masayuki Goto

    The 13th Asia Pacific Industrial Engineering and Management Systems Conference (APIEMS 2012)   APIEMS 2012 ( No.253-1 )  2012.12  [Refereed]

  • An Optimal Weighting Method by Using the Category Information in Text Classification based on Metric Learning

    Kenta Mikawa, Takashi Ishida, Masayuki Goto

    The 13th Asia Pacific Industrial Engineering and Management Systems Conference (APIEMS 2012)   APIEMS 2012 ( No.25-1 )  2012.12  [Refereed]

    Authorship:Last author

  • A Study of Document Classification Based on Polya Mixture Distribution

    Gendo Kumoi, Kenta Mikawa, Masayuki Goto, Shigeichi Hirasawa

    The 13th Asia Pacific Industrial Engineering and Management Systems Conference (APIEMS 2012)   APIEMS 2012 ( No.136-1 )  2012.12  [Refereed]

  • English and Japanese Text Categorization Using Word and Character N-grams

    Makoto Suzuki, Naohide Yamagishi, Yi-Ching Tsai, Masayuki Goto

    The 13th Asia Pacific Industrial Engineering and Management Systems Conference (APIEMS 2012)   APIEMS 2012  2012.12  [Refereed]

  • A Study of Recommender System to Improve Aggregate Diversity based on Latent Class Model

    Takeshi Suzuki, Kenta Mikawa, Masayuki Goto

    The 13th Asia Pacific Industrial Engineering and Management Systems Conference(APIEMS 2012)   APIEMS 2012 ( No.122-1 )  2012.12  [Refereed]

  • A Study of Recommender System based on Mixed and Constrained Latent Dirichlet Allocation

    Shunsuke Sakamoto, Yusuke Izawa, Kenta Mikawa, Masayuki Goto

    The 13th Asia Pacific Industrial Engineering and Management Systems Conference (APIEMS 2012)   APIEMS 2012 ( No.144-1 )  2012.12  [Refereed]

  • A Proposal of Improved Naive Bayes Method for Collaborative Filtering by Introducing Clustering

    Takahiro Oi, Kenta Mikawa, Takashi Ishida, Masayuki Goto

    The 13th Asia Pacific Industrial Engineering and Management Systems Conference (APIEMS 2012)   APIEMS 2012 ( No.172-1 )  2012.12  [Refereed]

  • A Proposal of Extracting Unknown Information from Customer Review for SWOT Analysis

    Kenta Mikawa, Gendo Kumoi, Kazuma Suzuki, Masayuki Goto

    2011 Asian Conference of Management Science & Applications (ACMSA2011)   ACMSA2011  2011.12  [Refereed]

  • A Predictive Model of Number of Customers for Restaurant Chain Based on Bayes Optimal Mixture

    Masayuki Goto, Yoichi Komiya, Takashi Ishida, Tadayuki Masui

    2011 Asian Conference of Management Science & Applications (ACMSA2011)   ACMSA2011  2011.12  [Refereed]

  • A Prediction Method Based On Mixture Decision Tree for Continuous Variable

    Takashi Ishida, Takuya Sakaguchi, Masayuki Goto

    2011 Asian Conference of Management Science & Applications (ACMSA2011)   ACMSA2011  2011.12  [Refereed]

  • Korean text categorization using the character TV-gram

    Makoto Suzuki, Naohide Yamagishi, Masayuki Goto

    7th International Conference on Information Technology and Application, ICITA 2011   ( ICITA 2011 ) 197 - 202  2011.11  [Refereed]

    Authorship:Last author

     View Summary

    We previously proposed the accumulation method, a language-independent text classification method that is based on the character N-gram, and classified English and Japanese text documents. The accumulation method does not depend on the language structure, because it uses the character N-gram to form Index Terms. If text documents are expressed in Unicode, the accumulation method can classify the documents using the same algorithm. In the present paper, we improve the proposed method and classify Korean text documents, which are newspaper articles from the Korean Hankyoreh 2008 data set. As a result, the highest macro-averaged F-measure of the proposed method is 90.2% for the Korean Hankyoreh 2008 data set. In this way, we obtain good results for Korean. In addition, we demonstrate the improvement in classification accuracy for English. Finally, we consider points of qualitative meaning of the accumulation method.

  • A Proposal of Extended Cosine Measure for Distance Metric Learning in Text Classification

    Kenta Mikawa, Takashi Ishida, Masayuki Goto

    2011 IEEE International Conference on Systems, Man, and Cybernetics    2011.10  [Refereed]

    Authorship:Last author

    Research paper, summary (international conference)  

    DOI

  • Multivalued Document Classification by Maximization of Posterior Probability Based on Relevance Vector Machine

    Ryosuke Odai, Gendo Kumoi, Kenta Mikawa, Gou Hosoya, Masayuki Goto

    The 12th Asia Pacific Industrial Engineering and Management Systems Conference   APIEMS2011  2011.10  [Refereed]

  • Automated Source Code Plagiarism Detection Based on Coding Style Model

    Kenta Hibi, Gendo Kumoi, Kenta Mikawa, Masayuki Goto

    The 12th Asia Pacific Industrial Engineering and Management Systems Conference   APIEMS2011  2011.10  [Refereed]

  • An Optimal Weighting Method in Supervised Learning of Linguistic Model for Text Classification

    Kenta Mikawa, Takashi Ishida, Masayuki Goto

    The 12th Asia Pacific Industrial Engineering and Management Systems Conference   APIEMS2011  2011.10  [Refereed]

  • A Study on the Recommender System Based on Probabilistic Latent Model

    Takeshi Suzuki, Gendo Kumoi, Kenta Mikawa, Masayuki Goto

    The 12th Asia Pacific Industrial Engineering and Management Systems Conference   APIEMS2011  2011.10  [Refereed]

  • A Study on Automatic Summarization of Customer Reviews Based on Maximum Coverage Problem

    Takashi Takemura, Motomichi Kumoi, Gou Hosoya, Masayuki Goto

    The 12th Asia Pacific Industrial Engineering and Management Systems Conference   APIEMS2011  2011.10  [Refereed]

  • A Recommender System Considering with Item Evaluation based on Mixed Membership Stochastic Block Models

    Yusuke Izawa, Hayato Sakaeda, Kenta Mikawa, Masayuki Goto

    The 12th Asia Pacific Industrial Engineering and Management Systems Conference   APIEMS2011  2011.10  [Refereed]

  • Investigation of the Comfort Temperature in Traditional Houses of Nepal

    Itaru Sugano, Hom Bahadur Rijal, Akira Okada, Masayuki Goto, Brenda Bushell

    The 17th International Interdisciplinary Conference on the Environment    2011.06  [Refereed]

    Research paper, summary (international conference)  

  • Investigation into the Water Quality of the Rivers in Kathmandu Valley and Local People’s Attitude Toward the Rivers

    Masashi Kobatake, Akira Okada, Hom B. Rijal, Hiromi Kobori, Masayuki Goto, Brenda Bushell

    The 17th International Interdisciplinary Conference on the Environment    2011.06  [Refereed]

    Research paper, summary (international conference)  

  • English and taiwanese text categorization using N-gram based on Vector Space Model

    Makoto Suzuki, Naohide Yamagishi, Yi Ching Tsai, Takashi Ishida, Masayuki Goto

    ISITA/ISSSTA 2010 - 2010 International Symposium on Information Theory and Its Applications   ( ISITA2010 ) 106 - 111  2010.10  [Refereed]

     View Summary

    In this paper, we present a new mathematical model based on a "Vector Space Model" and consider its implications. The proposed method is evaluated by performing several experiments. In these experiments, we classify newspaper articles from the English Reuters-21578 data set, and Taiwanese China Times 2005 data set using the proposed method. The Reuters-21578 data set is a benchmark data set for automatic text categorization. It is shown that FRAM has good classification accuracy. Specifically, the micro-averaged F-measure of the proposed method is 94.5% for English. However, that is 78.0% for Taiwanese. Though the proposed method is language-independent and provides a new perspective, our future work is to improve classification accuracy for Taiwanese. © 2010 IEEE.

    DOI

  • On a New Model for Automatic Text Categorization Based on Vector Space Model

    Makoto Suzuki, Naohide Yamagishi, Takashi Ishida, Masayuki Goto, Shigeichi Hirasawa

    IEEE International Conference on Systems, Man, and Cybernetics 2010   ( SMC2010 ) 3152 - 3159  2010.10  [Refereed]

    Research paper, summary (international conference)  

    DOI

  • A Pilot Study for the Construction of Sustainable Indicators in Rural Nepa

    Nozomi Imai, Mari Naitoh, Brenda Bushell, Masayuki Goto

    The 16th International Interdisciplinary Conference on the Environment    2010.09  [Refereed]

    Research paper, summary (international conference)  

  • Educating for Sustainability: A Pilot Study in an Elementary School in Rural Nepa

    Ryoko Iwamura, Momoko Ozawa, Brenda Bushell, Masayuki Goto

    The 16th International Interdisciplinary Conference on the Environment    2010.09  [Refereed]

    Authorship:Last author

    Research paper, summary (international conference)  

  • Estimation and Assignment Methods of CO2 Emissions

    Tetsuo Yamada, Masayuki Goto, Tadayuki Masui, Tomokazu Yoshifuji

    The 2nd International Workshop on Institutional Supply Chain Management   ISCM2009  2009.08  [Refereed]

  • Real Time Assignment of CO2 Emissions in Transportation Process -A System Development and Analysis of Information System with RFID-

    Tomokazu Yoshifuji, Masayuki Goto, Tetsuo Yamada, Tadayuki Masui

    The 14th International Symposium on Logistics   ISL2009  2009.07  [Refereed]

  • Real Time Assignment of CO2 Emissions in Transportation Process - A Process Improvement by Information System with RFID-

    Tetsuo Yamada, Masayuki Goto, Tadayuki Masui, Tomokazu Yoshifuji

    The 14th International Symposium on Logistics   ISL2009  2009.07  [Refereed]

  • Student questionnaire analyses using the clustering method based on the PLSI model

    Takashi Ishida, Hisashi Hamada, Gendo Kumoi, Masayuki Goto, Shigeichi Hirasawa

    The 2009 International Conference in Management Sciences and Decision Making   ICMSDN2009  2009.05  [Refereed]

    CiNii

  • Refinement of feature terms and improvement of classification accuracy on multilingual text categorization using character N-gram

    Makoto Suzuki, Yi-Ching Tsai, Takashi Ishida, Masayuki Goto, Shigeichi Hirasawa

    The 2009 International Conference in Management Sciences and Decision Making   ICMSDN2009  2009.05  [Refereed]

  • Document classification methods with a small-size training set

    Gendoh Kumoi, Takashi Ishida, Masayuki Goto, Shigeichi Hirasawa

    The 2009 International Conference in Management Sciences and Decision Making   ICMSDN2009  2009.05  [Refereed]

  • Asymptotic evaluation of distance measure on high dimensional vector spaces in text mining

    Masayuki Goto, Takashi Ishida, Makoto Suzuki, Shigeichi Hirasawa

    2008 International Symposium on Information Theory and its Applications, ISITA2008   ( ISITA2008 )  2008.10  [Refereed]

    Authorship:Lead author

     View Summary

    This paper discusses the document classification problems in text mining from the viewpoint of asymptotic statistical analysis. In the problem of text mining, the several heuristics are applied to practical analysis because of its experimental effectiveness in many case studies. The theoretical explanation about the performance of text mining techniques is required and such thinking will give us very clear idea. In this paper, the performances of distance measures used to classify the documents are analyzed from the new viewpoint of asymptotic analysis. We also discuss the asymptotic performance of IDF measure used in the information retrieval field.

    DOI

  • Refinement of index term set and improvement of classification accuracy on text categorization

    Makoto Suzuki, Takashi Ishida, Masayuki Goto

    2008 International Symposium on Information Theory and its Applications, ISITA2008   ( ISITA2008 )  2008.10  [Refereed]

    Authorship:Last author

     View Summary

    In our previous paper, we proposed a new classification technique called the Frequency Ratio Accumulation Method (FRAM). This is a simple technique that adds up the ratios of term frequency among categories. However, in FRAM, the use of index terms is unlimited. Then, we adopt Character TV-gram as index terms improving the above-described particularity of FRAM. In the present paper, we will refine the DB of the index term set using mutual information and frequency ratio, and improve the classification accuracy. Next, the proposed method is evaluated by performing several experiments. In these experiments, we classify newspaper articles from English Reuters-21578 using FRAM. Reuters-21578 provides benchmark data in automatic text categorization. As a result, we show that it has the good classification accuracy. Specifically, the macro-averaged F-measure of the proposed method is 92.3% for Reuters-21578. Our method is language-independent and provides a new perspective and has excellent potential.

    DOI

  • Information Modeling to Calculate CO2 Emissions Caused by Distribution and Its Allocations

    Tomokazu Yoshifuji, Masayuki Goto, Tetsuo Yamada, Tadayuki Masui

    The Proceedings of the 13th International Symposium on Logistics   ISL2008   546 - 554  2008.07  [Refereed]

    Research paper, summary (international conference)  

  • A Study on Strategy for Improvement of Customer Purchasing Quantity to Realize Efficient Green Logistics in Home Delivery Business

    Miho Suzuki, Tomoe Tomita, Masayuki Goto, Tadayuki Masui

    The Proceedings of the 13th International Symposium on Logistics   ISL2008   538 - 545  2008.07  [Refereed]

    Research paper, summary (international conference)  

  • Faculty Development by Student Questionnaire Analysis: A Class Partition Problem

    Shigeichi Hirasawa, Takashi Ishida, Masayuki Goto

    The 2008 International Conference in Management Sciences and Decision Making   ICMSDN2008  2008.06  [Refereed]

    Authorship:Last author

    Research paper, summary (international conference)  

    CiNii

  • Statistical evaluation of measure and distance on document classification problems in text mining

    Masayuki Goto, Takashi Ishida, Shigeichi Hirasawa

    CIT 2007: 7th IEEE International Conference on Computer and Information Technology   CIT2007   674 - 679  2007.10  [Refereed]

    Authorship:Lead author

     View Summary

    This paper discusses the document classification problems in text mining from the viewpoint of asymptotic statistical analysis. By formulation of statistical hypotheses test which is specified as a problem of text mining, some interesting properties can be visualized. In the problem of text mining, the several heuristics are applied to practical analysis because of its experimental effectiveness in many case studies. The theoretical explanation about the performance of text mining techniques is required and this approach will give us very clear idea. The distance measure in word vector space is used to classify the documents. In this paper, the performance of distance measure is also analized from the new viewpoint of asymptotic analysis. © 2007 IEEE.

    DOI

  • A Survey on the Customer's Sense Towards Environmental Logistics and Its Application to the Sales and Delivery System

    Kaori Suzuki, Tadayuki Masui, Masayuki Goto, Hideki Nakahara

    The proceedings of the 12th International Symposium on Logistics   ( ISL2007 )  2007.07  [Refereed]

    Research paper, summary (international conference)  

  • An Evaluation of Joint Delivery System from the Viewpoint of EAn Evaluation of Joint Delivery System from the Viewpoint of Environmental Logisticsnvironmental Logistics

    Yumi Kurishima, Akihisa Tanda, Masayuki Goto, Tadayuki Masui

    The proceedings of the 11th International Symposium on Logistics   ( ISL2006 )  2006.07  [Refereed]

    Research paper, summary (international conference)  

  • A Study on the Logistic System with Environmental Efficiency and Economic Effectiveness

    Masayuki Goto, Tadayuki Masui, Nobuyuki Kawai

    The proceedings of the 11th International Symposium on Logistics   ( ISL2006 )  2006.07  [Refereed]

    Authorship:Lead author

    Research paper, summary (international conference)  

  • Creating Learning Environments through Eco-design

    Brenda Bushell, Yumi Kurishima, Kenta Mikawa, Masayuki Goto

    North American Association for Environmental Education, 34th Annual Conference    2005.10  [Refereed]

    Authorship:Last author

    Research paper, summary (international conference)  

  • The Model of Analysis in the Information Chain

    Nobuyuki Kawai, Tadayuki Masui, Masayuki Goto

    International Congress on Logistics and SCM Systems     161 - 166  2004.11  [Refereed]

  • Representation Method for a Set of Documents from the Viewpoint of Bayesian Statistics

    Masayuki Goto, Takashi Ishida, Shigeichi Hirasawa

    2003 IEEE International Conference on System, Man and Cybernetics   ( IEEE SMC 2003 ) 4637 - 4642  2003.10  [Refereed]

    Authorship:Lead author

    Research paper, summary (international conference)  

    DOI

  • Consistency of Bayesian Model Selection

    Masayuki Goto, Toshiyasu Matsushima, Shigeichi Hirasawa

    International Symposium on Information Theory and Its Applications   ISITA98  1998.10  [Refereed]

    Authorship:Lead author

    Research paper, summary (international conference)  

  • On Error Rates of Statistical Model Selection Based on Information Criteria

    Masayuki Gotoh, Toshiyasu Matsushima, Shigeichi Hirasawa

    IEEE International Symposium on Information Theory   ISIT98  1998.08  [Refereed]

    Authorship:Lead author

    Research paper, summary (international conference)  

    DOI

  • On Theory and Application of Statistical Model Selection Based on Bayes Decision Theory

    Masayuki Gotoh, Toshiyasu Matsushima, Shigeichi Hirasawa

    International Conference on Production Research   ICPR97  1997.08  [Refereed]

    Authorship:Lead author

    Research paper, summary (international conference)  

  • A Study on Difference of Codelengths between MDL Codes and Bayes Codes on Case Different Priors Are Assumed

    Masayuki Gotoh, Toshiyasu Matsushima, Shigeichi Hirasawa

    IEEE International Symposium on Information Theory   ISIT97  1997.06  [Refereed]

    Authorship:Lead author

    Research paper, summary (international conference)  

  • String Matching Algorithmによる有歪み圧縮について

    小幡洋昭, 後藤正幸, 平澤茂一

    電子情報通信学会技術研究報告   IT96-60  1997.01

  • An Analysis on Difference between the Code based on MDL Principle and the Bayes Code

    Masayuki Gotoh, Toshiyasu Matsushima, Shigeichi Hirasawa

    International Symposium on Information Theory and Its Application   ISITA96  1996.10  [Refereed]

    Authorship:Lead author

    Research paper, summary (international conference)  

  • A Fast Learning Algorithm for Multilayer Neural Networks

    Yasutaka Kainuma, Masayuki Gotoh, Nobuhiko Tawara

    International Conference on Production Research   ICPR93   487 - 490  1993.08  [Refereed]

    Research paper, summary (international conference)  

    CiNii

▼display all

Industrial Property Rights

  • 画像アノテーション装置、画像アノテーション方法および画像アノテーションプログラム

    井田安俊, 竹内 亨, 寺本純司, 八木哲志, 後藤正幸, 中澤真, 梅澤克之

    Patent

  • 分析装置、 分析方法及び分析プログラム

    井田安俊, 寺本純司, 八木哲志, 後藤正幸, 中澤真, 梅澤克之

    Patent

  • 学習支援装置、学習支援方法及び学習支援プログラム

    井田安俊, 藤原靖宏, 後藤正幸, 中澤真, 梅澤克之

    Patent

  • “問題提示装置、問題提示システム、問題提示方法、及び問題提示プログラム

    藤原靖宏, 木原誠司, 井田安俊, 後藤正幸, 中澤真, 梅澤克之

    Patent

  • 分析システム及び分析方法

    藤原靖宏, 井田安俊, 後藤正幸, 中澤真, 梅澤克之

    Patent

  • 学習支援システム、学習支援装置および学習支援方法

    岩村相哲, 藤原靖宏, 井田安俊, 後藤正幸, 中澤真, 梅澤克之

    Patent

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Syllabus

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Sub-affiliation

  • Faculty of Commerce   School of Commerce

  • Faculty of Science and Engineering   Graduate School of Creative Science and Engineering

Research Institute

  • 2023
    -
    2024

    Waseda Center for a Carbon Neutral Society   Concurrent Researcher

  • 2023
    -
    2024

    Center for Data Science   Concurrent Researcher

  • 2022
    -
    2024

    Waseda Research Institute for Science and Engineering   Concurrent Researcher

  • 2020
    -
    2025

    Institute of Data Science   Director of Research Institute

Internal Special Research Projects

  • 大規模ログデータを活用したビジネス施策の効果推定と施策パラメータの最適化

    2023   山下 遥, 楊 添翔, 山極 綾子

     View Summary

    本研究では,ECサイトなどに蓄積される顧客ユーザの活動履歴データ(大規模ログデータ)を活用してビジネス施策の効果の特定と施策パラメータを最適化するモデルの開発を目的としている。多くのECサイトでは,優良顧客の維持や顧客の成長を目的とした割引クーポンやポイント付与などの施策を実施しており,その最適化は重要な課題の一つと言える。一方で,過去にどの顧客ユーザに対してどのような施策が行われているのかといった履歴データは蓄積されており,それらを有効に活用して,より効果的な施策デザインに結び付けようとする試みも始まっている。本研究では,近年高度に発展した機械学習を効果的に活用して施策評価を行う手法について検討を行うと共に,施策パラメータの最適化のためのビジネス施策実験の計画手法の開発を目指している。人工知能や機械学習の分野では,データの選択バイアスなどの考慮し統計的因果関係を学習できる機械学習モデルや,実際には実施していない施策を実施したときの効果を推定しようとした反実仮想機械学習というモデルも提案されている。本研究ではこれらの人工知能分野における研究動向を踏まえ,過去のログデータを学習した機械学習モデルに基づくビジネス施策評価のための手法を構築し,人工データや実データを用いて評価を行った。加えて,大規模ログデータを有効活用しつつ,新たな施策実験を通じて,ビジネス施策を精度よく評価し,施策効果の高い対象グループを特定する手法を開発してその評価を行った。その結果,規模ログデータを活用したビジネス施策の効果推定と施策パラメータの最適化のための様々なモデルを提案することができ,ビジネスドメインにおける機械学習活用モデルに関する重要な知見の蓄積に繋がった。本研究の成果は,国内学会や国際会議の他,客観的な効果が認められた成果については順次,投稿論文として投稿を進める予定である。

  • 大規模ログデータの機械学習に基づく施策評価に関する課題検討

    2022   山下 遥

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    本研究では,機械学習に基づく施策評価に関して体系的,かつ俯瞰的な観点から課題検討を行った。多くのビジネス施策の最適化問題において,大量に蓄積された大規模ログデータを有効活用し,機械学習モデルや人工知能技術を用いて分析し,施策評価に結び付けようとする動きがある。本研究ではまず,俯瞰的な観点から課題を整理し,機械学習モデルによる大規模ログデータの学習と新たな実験データを統合的に分析する枠組み構築に向けた課題を整理した。その上で,ログデータから構築される機械学習を効果的に用いた施策評価を改善する新たな手法を検討し、人工データや実データを用いた実験を通じた評価を行い、様々な成果を得た。

  • データ駆動型アプローチに基づく顧客-企業の有機的発展モデルに関する基礎研究

    2021   三川健太, 山下 遥

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    本研究では,近年急速に注目度が高まっている“データ駆動型ビジネス”をある種の社会システムと捉え,顧客行動と企業活動を要素とする有機的発展モデルを構築すると共に,様々な企業の事例に適用し,実証的な評価を行なった。具体的には,複数の企業と連携し,様々なビジネスの現場で生成されるビジネスデータを最大限活用する方法論について実証的な研究を行い,先進的なAI・機械学習手法の効果的な活用技術を開発した.その過程では,ビジネスアナリティクスに必要となる,様々なデータ分析技術を提案している.加えて,本研究の成果は様々な実務で検証を行っており,その有効性が検証されている。

  • 実データを対象としたデータサイエンスの高度化に関する研究

    2020   三川 健太, 山下 遥, 楊 添翔, 雲居 玄道

     View Summary

    本研究では、様々な企業活動で生成される多様な実データを対象とし、これらの経営資源としてのデータを最大限に活用して価値に結び付けるためのデータサイエンスの分析技術とモデルについて研究を行った。本研究では、先進的な機械学習モデル等を駆使した分析技術をベースとし、さらに実応用において利用価値の高い分析モデルの開発と実データを用いた検証を行った。具体的な事例として、ECサイトの購買・閲覧履歴データ分析、インターネットの閲覧履歴データ分析、クレジットカード利用履歴データ分析、小売店舗の販売履歴データ分析などの実応用課題と扱い、様々な分析モデルを提案すると共に、その評価を行った。

  • ネパールの特性を考慮した社会システムモデルに関する基礎研究

    2019   山下 遥

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    本研究は,南アジアの発展途上国の一つであるネパールを対象とし,現地のフィールド調査と収集したデータの分析を通じ,ネパールの社会的課題を抽出すると共に,社会システムモデルのあるべき姿へ向けた議論を展開することが目的である。特に,ネパール人の仕事感と経済的活動に関する問題,ネパール人女性のエンパワーメントに関する問題,世界遺産の保全に関する問題など,いくつかの具体的問題について,カトマンズとチトワン国立公園周辺でフィールド調査を実施した。得られたデータの分析とネパールのNational Collegeの研究グループとの密な議論に基づき,重要課題やその地域差を抽出すると共に,今後取り得る施策を示した。

  • 大規模データから生成される統計モデルに基づく最適化モデリングに関する研究

    2018   三川 健太, 山下 遥

     View Summary

    本研究課題では、過去の履歴データを学習して得られる統計モデルを用いて、制御変数の最適化を行う方法論について基礎的な研究を行った。特に、中古品販売を行うECサイトにおける各アイテムの出品価格最適化問題を事例として扱い、過去の売り上げ実績データを機械学習し、適正な出品価格を求める方法について検討した。過去の売り上げデータはそのアイテムが出品された状況での売り上げ結果であり、出品価格を変化させた場合の挙動についてはデータが与えられていない。そのため、何らかの統計的推測と追加実験を組み合わせた方法を検討する必要があり、本研究では、そのようなモデルをいくつかの観点から提案し、評価を行った。

  • ネパールをフィールドとしたアクティブラーニングに基づく経営工学教育モデル

    2018   山下 遥, Brenda Bushell, 柳生 修二

     View Summary

    本研究は,アジアの発展途上国の一つであるネパールをフィールドとし,発展途上にある国における様々な経済的課題・社会的課題を考慮しつつ,経営工学的な観点から調査・分析する活動を通じ,参加学生が自ら主体的に問題解決能力を養うアクティブラーニングを可能とした実践的な経営工学教育プログラムを設計すると共に,実施に結びつけ,実証的な方法によって評価を行うことを目的としている。本研究では,経営システム工学科の学生を対象としたネパール研修プログラムを設計すると共に,ネパール現地での研修プログラムを実施し,その参加度の評価と共に,プログラム期間中のアンケートなどを通じたプログラム評価を行った。

  • 統計的学習モデルに基づくECサイト上のユーザ行動パターン分析手法の開発

    2017   三川 健太, 山下 遥

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    本研究では,インターネット上のECサイト等のデータベースに蓄積されるユーザの大規模行動履歴データに基づき,ユーザ行動分析のための統計的学習モデルを構築し,経営判断やマーケティングツールとして活用するための次世代パターン認識手法の開発を行なった。また,実際のいくつかの実データ分析に適用し,実応用の側面から評価を行なった。特に,ユーザ行動履歴の時系列パターン分析モデルに対して,ユーザとアイテムの異質性を表現する潜在クラスを導入して,新たな分析モデルを提案した。これらの成果は,情報処理学会論文誌などのいくつかの論文誌や国内外の学会において発表を行なった。

  • ネパールにおける持続可能な事業経営モデルを素材とした教育プログラムに関する研究

    2017   山下 遙

     View Summary

    本研究では,アジアの発展途上国の一つであるネパールをフィールドとし,様々な経済的課題・社会的課題について,経営工学的な観点から調査・分析する活動を通じ,参加学生が自ら主体的に問題解決能力を養うアクティブラーニングを可能とした実践的な経営工学教育プログラムを設計し,実証的な評価を行なった。2017年3月に実施したネパールフィールド研修プログラムでは,両国間の教育システムの差異,並びにネパールにおけるICT活用法に関する分析調査を設計し,実際に現地における調査とグループディスカッション等による議論の深化を図った。また,本プログラムの実施を通じた実証的評価と学生アンケートによる事後評価を行なった。

  • ネパールをフィールドとした実践的経営工学教育プログラムの開発と運用

    2016   山下 遥

     View Summary

    本研究では,アジアの発展途上国の一つであるネパールをフィールドとし,発展途上にある国における様々な経済的課題・社会的課題を考慮しつつ,経営工学的な観点から調査・分析する活動を通じ,参加学生が自ら主体的に問題解決能力を養うアクティブラーニングを可能とした実践的な経営工学教育プログラムを設計すると共に,実施に結びつけ,実証的な方法によって評価を行った.具体的には,ネパールにおけるICT活用,並びに初等教育の問題を題材に,それらの現状と将来展望について現地フィールド調査を設計し,データ分析とグループ討論を行うプログラムを設計し,実施を通じてその効果を検証した.

  • ネパール固有の事業経営モデルを活用した教育モジュールに関する研究

    2013  

     View Summary

    本研究では,発展途上国の一つであるネパールを対象とし,環境や社会に配慮しつつ,経済的に継続発展可能なネパール型の環境ビジネスモデルを事例として導入することにより,大学生向け経営工学教育モジュールを構築することを目的としている.本研究課題の成果は,経営システム工学科における正規科目「経営実践・海外プロジェクト(2 単位)」の一プログラムであるネパール研修プログラムにおいて実践し,評価を行っている.このネパール研修プログラムは2013年3月に初めて早稲田大学創造理工学部経営システム工学科の学生6名の参加を得てスタートし,2014年3月にやはり学生6名の参加を得て第二回の現地研修プログラムを実施した.この研修プログラムでは,発展途上国であるネパールをフィールドとし,ネパールにおける循環型発展社会システムを実現する方法を探求し,ネパール型環境ビジネスモデルの構築を試みるプロセスを通じて,環境配慮型ビジネスモデルの在り方に加え,統計分析やシミュレーション技法の習得や理解を促進する教育の場を提供している.すなわち,経営システム工学を専門とする学生に対して,日本では得られない新たな学びを提供する場を提供するための教育モジュールである.2013年度は,そのための教育モジュールを構築するため,特にネパールの豊かな観光資源を活用しつつ,持続的発展可能な観光ビジネスを構築するためのフィールド調査を設計し,教育プログラムに組み込んでいる.このフィールド調査では,ネパールの首都カトマンズと世界遺産であるダルバールスクエアに加え,カトマンズ盆地の避暑地として観光スポットとなっているナガルコットにおいてアンケート形式の現地調査を実施している.その際,現地住民に対してネパール語と英語間の通訳をNational Collegeに依頼し,早稲田大学の学生とNational Collegeの学生が2人1ペアでフィールド調査を実施することで200件近いデータを収集した.これらのフィールド調査に基づき,毎晩のグループディスカッションを通じ,様々な課題や持続可能な観光ビジネスのあり方について検討を実施している.また,その他,女性企業家による活動や都市部のごみ処理マネジメント,小学生対象の観光産業開発教育プログラムの設計と実施等のアクティビティを組み合わせ,2014年2月27日~3月10日のネパール研修プログラムを無事に終了することができた.このプログラムの評価は,参加学生へのアンケート評価によって実施している.また,現地のフィールド調査で収集した多くのデータを日本に持ち帰ってきており,今後,これらのデータに対して統計的な解析を進め,国内学会や国際会議にて発表する予定である.

  • 情報源符号化と統計的モデル化の基礎理論

    1998  

     View Summary

     本研究では、1980年代から議論が活発になってきた統計的モデル化と情報源符号化の問題を統一的に扱い、その基礎理論の構築を目的としている。その際、ベイズ統計理論の枠組を用い、広いクラスの確率モデル族に対して成立する本質的な性質を議論した。 まず、確率分布を連続パラメータで規定するパラメトリックモデル族に対し、ある正則条件のもとで、事後確率密度は漸近的に正規分布に収束する事実を精密化した。この事後確率密度の漸近正規性は以前から議論がなされていたが、密度の一様収束などより強い結果を導いている。さらに、この結果を用い、統計的モデル選択の一致性、誤り率の上界の評価、ベイズ符号の符号長の漸近評価を行なった。この結果、ベイズ統計に基づく統計的モデル化と情報源符号化の性能の漸近的評価が可能となり、一応の成果を得ることができた。 さらに、最近提案された拡張確率的コンプレキシティ(ESC)においても、同様の漸近正規性を導き、ESCの漸近式を導出した。これは、確率モデルの良さを様々な損失関数で測ろうとする基準であり、その一般化の中にも符号化と本質的に同じ性質を見い出すことができる。 以上のように、本研究は統計的モデル化、情報源符号化を統一的視点から整理し、本質的な性質を議論することで一般的な評価を行ない、その成果を得ることができた。

  • 情報源符号化と統計的モデル選択の基礎理論

    1997  

     View Summary

    近年、情報源符号化と統計的学習理論を関連づける研究において、ベイズ統計理論が中心的な役割を演じていることが指摘されている。とくに、J.Rissanen の提唱した MDL、あるいは Stochastic Complexity の概念はベイズ理論と密接に関係する。本研究では、統計的モデル選択、ベイズ的学習、情報源符号化をベイズ統計の枠組みから体系的にとらえ、その普遍的性質の解明および現実問題への適用を目的としており、主に以下のような成果を得た。(1) ベイズ決定理論に基づくモデル選択の漸近一致性の解析(2) ベイズ決定理論に基づくモデル選択の選択誤り率の上界の解析(3) 情報量規準に基づくモデル選択の選択誤り率の上界の解析(4) ベイズ符号(混合モデル)の漸近的性質の解析(1)に関しては、まずベイズ決定理論に基づき統計的モデル選択問題を一般的に定式化し、その漸近的一致性について考察した。前年の結果では、予測を目的とする場合でも、漸近的一致性を持つことが明らかとなっていたが、本年度はさらに一致性を弱一致性と強一致性に分け、どのような条件のもとで両者の差異が生じるかを論じた。(2)では、主に真のモデルを発見することを目的とするベイズ規準によるモデル選択に対し、その誤り率の上界を導出できた。(3)では、情報量規準を用いたモデル選択に対し、その誤り率の上界を導出できた。(4)では、Clarke and Barron の漸近解析に関して、階層モデル族であるFSMXモデルへの拡張を検討し、ベイズ符号の漸近符号長、漸近平均符号長を示した。なお、本研究による1997年度の研究発表は以下の通りである。[1] A Study on Difference of Codelengths between MDL Codes and Bayes Codes on Case Different Priors Are Assumed, IEEE International Symposium on Information Theory 97, (1997,6)[2] ベイズ決定理論に基づく統計的モデル選択について、 電子情報通信学会 技術研究報告 IT97-21, (1997,7) [3] On Theory and Application of Statistical Model Selection Based on Bayes Decision Theory, ICPR Production Research 97, (1997,8)[4] 階層モデル族に対するモデル選択の選択誤り率について, 電子情報通信学会 技術研究報告 IT97, (1998,1)

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