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重み付き学習ベイジアンネットワークを用いた欠損値補完手法

重み付き学習ベイジアンネットワークを用いた欠損値補完手法

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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: 2012/02/01

タイトル(英語): Missing Value Imputation Method by Using Bayesian Network with Weighted Learning

著者名: 宮越 喜浩(名古屋工業大学),加藤 昇平(名古屋工業大学)

著者名(英語): Yoshihiro Miyakoshi (Nagoya Institute of Technology), Shohei Kato (Nagoya Institute of Technology)

キーワード: クラス判別,情報欠損,ベイジアンネットワーク,重み付き学習,K2アルゴリズム  Classification,Missing Value,Bayesian Network,Weighted Learning,K2 Algorithm

要約(英語): Recently, we can easily have huge database with the development of computer network. Accordingly, it becomes difficult for users to extract knowledge from the database. In this paper, we focus on data mining, especially classification. In the real-world data mining, missing value problem is happened, for example, speech containing noises, facial occlusions, and so on. When the test sample have missing values, classification systems can not classify that. In previous studies, various imputation methods have been developed. Previous imputation methods were developed to solve the missing value problem with lots of explanatory variable, even if some explanatory variables are ineffective for imputation. It has been said that using lots of variable deteriorates in learning efficiency, thus we believe that imputation methods should be developed considering relations among explanatory variables. Moreover, it is effective considering not only relations among explanatory variables but also between the test sample and each of the training sample. Therefore we propose the imputation method by using Bayesian network with weighted learning. Through the experiments, we could confirm that the proposed method imputed missing values with approximate values, and a classification system successfully classified the test sample, in which missing values were imputed by the proposed method, in comparison with some conventional methods.

本誌: 電気学会論文誌C(電子・情報・システム部門誌) Vol.132 No.2 (2012) 特集:多様な情報社会に適応するシステム技術

本誌掲載ページ: 299-305 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/132/2/132_2_299/_article/-char/ja/

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