2015年
Principal Sensitivity Analysis
ADVANCES IN KNOWLEDGE DISCOVERY AND DATA MINING, PART I
- ,
- ,
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- 巻
- 9077
- 号
- 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
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- 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
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- DOI : 10.1007/978-3-319-18038-0_48
- ISSN : 0302-9743
- DBLP ID : conf/pakdd/KoyamadaKNI15
- Web of Science ID : WOS:000361910400048