Presentation	2007-06-29 Classifier Fusion on the Basis of Data Selection and Feature Selection Satoshi SHIRAI, Mineichi KUDO,
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Abstract(in English)	In recent years, many approaches to gain high performance by combining some classifiers have been proposed. In the bagging, we exploit many random replicates of samples, and in the random subspace method we exploit randomly chosen feature subsets. In this paper, we introduce a method to select both at the same time. For a data subset chosen from a certain class, we select the most effective features, and repeat the procedure in a deterministic way. We compare the technique with the random subspace method.
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Keyword(in English)	Random subspace method / Bagging / Subclass method
Paper #	DE2007-13,PRMU2007-39
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Conference Information
Committee	DE
Conference Date	2007/6/21(1days)
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Paper Information
Registration To	Data Engineering (DE)
Language	JPN
Title (in Japanese)	(See Japanese page)
Sub Title (in Japanese)	(See Japanese page)
Title (in English)	Classifier Fusion on the Basis of Data Selection and Feature Selection
Sub Title (in English)
Keyword(1)	Random subspace method
Keyword(2)	Bagging
Keyword(3)	Subclass method
1st Author's Name	Satoshi SHIRAI
1st Author's Affiliation	Graduate School of Information Science and Technology Hokkaido University()
2nd Author's Name	Mineichi KUDO
2nd Author's Affiliation	Graduate School of Information Science and Technology Hokkaido University
Date	2007-06-29
Paper #	DE2007-13,PRMU2007-39
Volume (vol)	vol.107
Number (no)	114
Page	pp.pp.-
#Pages	6
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