Presentation 2005/3/23
Multi-class Pattern Classification based on a Probabilistic Model of Combining Binary Classifiers
Naoto YUKINAWA, Shigeyuki OBA, Kikuya KATO, Shin ISHII,
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Abstract(in English) We propose a novel probabilistic model for constructing a multi-class pattern classifier by weighted aggregation of binary classifiers, which has latent variables as class membership probabilities. We also derive a maximum likelihood algorithm to estimate the latent probability and hence the class membership. We apply our method to classification problems of synthetic data and a real world tumor data. We show that our method can achieve the comparative performance to heuristic voting methods.
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Keyword(in English) multi-class classification / binary classifier / gene expression analysis
Paper # NC2004-221
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Committee NC
Conference Date 2005/3/23(1days)
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Registration To Neurocomputing (NC)
Language JPN
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Title (in English) Multi-class Pattern Classification based on a Probabilistic Model of Combining Binary Classifiers
Sub Title (in English)
Keyword(1) multi-class classification
Keyword(2) binary classifier
Keyword(3) gene expression analysis
1st Author's Name Naoto YUKINAWA
1st Author's Affiliation Graduate School of Information Science, Nara Institute of Science and Technology()
2nd Author's Name Shigeyuki OBA
2nd Author's Affiliation Graduate School of Information Science, Nara Institute of Science and Technology
3rd Author's Name Kikuya KATO
3rd Author's Affiliation Research Institute, Osaka Medical Center for Cancer and Cardiovascular Diseases
4th Author's Name Shin ISHII
4th Author's Affiliation Graduate School of Information Science, Nara Institute of Science and Technology
Date 2005/3/23
Paper # NC2004-221
Volume (vol) vol.104
Number (no) 760
Page pp.pp.-
#Pages 6
Date of Issue