自己組織化写像を用いたSub-baggingによる欠損値データの推定

カテゴリ: 論文誌(論文単位)

グループ名: 【C】電子・情報・システム部門

発行日: 2017/08/01

タイトル(英語): Missing Value Estimation Using a Sub-bagging Model of Self-organizing Maps

著者名: 齊藤　史哲（青山学院大学理工学部）

著者名(英語): Fumiaki Saitoh (Department of Industrial and Systems Engineering,College of Science and Engineering, Aoyama Gakuin University)

キーワード: 自己組織化写像，欠損値推定，サブバギング，集団学習，前処理　　self-organizing map，missing value estimation，sub-bagging，ensemble learning，preprocessing

要約(英語): Missing value estimation is an important task in data mining and analysis of data containing missing values. The purpose of this study is to improve the accuracy of missing value estimation using self-organizing maps (SOMs), which have been studied in recent years. We have focused on the ensemble learning algorithms based on bootstrap sampling that have been successfully used in recent years, in cluster ensembles and pattern recognition. In the present study, in order to improve the accuracy of missing values estimation, we applied the bagging and sub-bagging major ensemble learning algorithms to SOM. We tested the effectiveness of the proposed methods through computational experiments using bench mark data sets published in the UCI Machine Learning Repository. The reproducibility error with respect to the artificial missing values was evaluated. The experimental results show that our methods were better in estimation using conventional SOM and simple ensemble of SOMs, from the viewpoint of the accuracy of missing value estimation. Further, sub-bagging was confirmed to tend to have higher accuracy than bagging

本誌: 電気学会論文誌C（電子・情報・システム部門誌） Vol.137 No.8 （2017） 特集：システム技術によるエネルギーの効率活用

本誌掲載ページ: 1102-1110 p

原稿種別: 論文／日本語

電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejeiss/137/8/137_1102/_article/-char/ja/