Presentation 2012-11-08
Efficient AUC Maximization by Approximate Reduction of Ranking SVMs
Daiki SUEHIRO, Kohei HATANO, Eiji TAKIMOTO,
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Abstract(in English) The formulation of Ranking SVMs is popular for maximizing AUC scores. More precisely, the formulation is given as a hard/soft margin optimization over pn pairs of p positive and n negative instances. Directly solving the problem is impractical since we have to deal with a sample of size pn, which is quadratically larger than the original sample size p+n. In this paper, we propose (approximate) reduction methods from the hard/soft margin optimization over pn pairs to variants of hard/soft margin optimization over p+n instances. The resulting classifiers of our methods are guaranteed to have a certain amount of margin over pn pairs.
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Keyword(in English) bipartite ranking / AUC / reduction / Ranking SVMs
Paper # IBISML2012-68
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Committee IBISML
Conference Date 2012/10/31(1days)
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Registration To Information-Based Induction Sciences and Machine Learning (IBISML)
Language ENG
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Efficient AUC Maximization by Approximate Reduction of Ranking SVMs
Sub Title (in English)
Keyword(1) bipartite ranking
Keyword(2) AUC
Keyword(3) reduction
Keyword(4) Ranking SVMs
1st Author's Name Daiki SUEHIRO
1st Author's Affiliation Department of Informatics, Kyushu University()
2nd Author's Name Kohei HATANO
2nd Author's Affiliation Department of Informatics, Kyushu University
3rd Author's Name Eiji TAKIMOTO
3rd Author's Affiliation Department of Informatics, Kyushu University
Date 2012-11-08
Paper # IBISML2012-68
Volume (vol) vol.112
Number (no) 279
Page pp.pp.-
#Pages 7
Date of Issue