Presentation	2000/9/18 S^3 Bagging : Fast Classifier Generation by Subsampling and Bagging Masahiro Terabe, Takashi Washio, Hiroshi Motoda,
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Abstract(in English)	In the data mining process, it is often necessary to induce classifiers iteratively until the human analysts complete extracting valuable knowledge from data. Therefore, the data mining tools need extract accurate knowledge from a large amount of data fast in responce to the human demand. One of the approaches to answer this request is to reduce the training data size by subsampling. In many cases, the accuracy of the induced classifiers becomes worse when the training data is subsampled. We propose S^3 Bagging(Small SubSampled Bagging) that adopts both subsampling and a method of committee learning, i.e., Bagging.
Keyword(in Japanese)	(See Japanese page)
Keyword(in English)	Bagging / subsampling / committee learning / data mining
Paper #	AI2000-33
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Committee	AI
Conference Date	2000/9/18(1days)
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Paper Information
Registration To	Artificial Intelligence and Knowledge-Based Processing (AI)
Language	JPN
Title (in Japanese)	(See Japanese page)
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Title (in English)	S^3 Bagging : Fast Classifier Generation by Subsampling and Bagging
Sub Title (in English)
Keyword(1)	Bagging
Keyword(2)	subsampling
Keyword(3)	committee learning
Keyword(4)	data mining
1st Author's Name	Masahiro Terabe
1st Author's Affiliation	Mitsubishi Research Institutes, Inc.()
2nd Author's Name	Takashi Washio
2nd Author's Affiliation	I.S.I.R, Osaka University
3rd Author's Name	Hiroshi Motoda
3rd Author's Affiliation	I.S.I.R, Osaka University
Date	2000/9/18
Paper #	AI2000-33
Volume (vol)	vol.100
Number (no)	321
Page	pp.pp.-
#Pages	7
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