Presentation 2000/7/12
Acquiring a Single Decision Tree Representation of Majority Voting Classifiers
Yasuhiro AKIBA, Shigeo KANEDA, Hussein ALMUALLIM,
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Abstract(in English) This paper addresses two problems in Majority voting classifiers(MVCs)like Bagging:(1)no logical reasoning behind the decision and(2)a large amount of classification time and space for significant accuracy boosting.To solve these problems, this paper proposes a method for learning a single decision tree that approximates MVCs.The method learns a DT from the original examples and the meta examples that are generated from each classifier joining MVCs.Experimental results show that the method has similar accuracy to Bagging and that the tree size by the method is as large as the size of two classifiers.
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Keyword(in English) Machine Learning / Decision Tree / Majority Voting / Accuracy Boosting / Averaging
Paper # OFS2000-26,AI2000-28
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Committee AI
Conference Date 2000/7/12(1days)
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Registration To Artificial Intelligence and Knowledge-Based Processing (AI)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Acquiring a Single Decision Tree Representation of Majority Voting Classifiers
Sub Title (in English)
Keyword(1) Machine Learning
Keyword(2) Decision Tree
Keyword(3) Majority Voting
Keyword(4) Accuracy Boosting
Keyword(5) Averaging
1st Author's Name Yasuhiro AKIBA
1st Author's Affiliation NTT Communication Science Labs.()
2nd Author's Name Shigeo KANEDA
2nd Author's Affiliation Doshisha Univ.
3rd Author's Name Hussein ALMUALLIM
3rd Author's Affiliation King Fahd Univ.of Petroleum & Minerals
Date 2000/7/12
Paper # OFS2000-26,AI2000-28
Volume (vol) vol.100
Number (no) 199
Page pp.pp.-
#Pages 8
Date of Issue