論文

査読有り
2015年

Principal Sensitivity Analysis

ADVANCES IN KNOWLEDGE DISCOVERY AND DATA MINING, PART I
  • Sotetsu Koyamada
  • ,
  • Masanori Koyama
  • ,
  • Ken Nakae
  • ,
  • Shin Ishii

9077
開始ページ
621
終了ページ
632
記述言語
英語
掲載種別
研究論文(国際会議プロシーディングス)
DOI
10.1007/978-3-319-18038-0_48
出版者・発行元
SPRINGER-VERLAG BERLIN

We present a novel algorithm (Principal Sensitivity Analysis; PSA) to analyze the knowledge of the classifier obtained from supervised machine learning techniques. In particular, we define principal sensitivity map (PSM) as the direction on the input space to which the trained classifier is most sensitive, and use analogously defined k- th PSM to define a basis for the input space. We train neural networks with artificial data and real data, and apply the algorithm to the obtained supervised classifiers. We then visualize the PSMs to demonstrate the PSA's ability to decompose the knowledge acquired by the trained classifiers.

リンク情報
DOI
https://doi.org/10.1007/978-3-319-18038-0_48
DBLP
https://dblp.uni-trier.de/rec/conf/pakdd/KoyamadaKNI15
Web of Science
https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=JSTA_CEL&SrcApp=J_Gate_JST&DestLinkType=FullRecord&KeyUT=WOS:000361910400048&DestApp=WOS_CPL
URL
http://dblp.uni-trier.de/db/conf/pakdd/pakdd2015-1.html#conf/pakdd/KoyamadaKNI15
ID情報
  • DOI : 10.1007/978-3-319-18038-0_48
  • ISSN : 0302-9743
  • DBLP ID : conf/pakdd/KoyamadaKNI15
  • Web of Science ID : WOS:000361910400048

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