Multi-class pattern classification based on a probabilistic model of combining binary classifiers

Authors:
Naoto Yukinawa;Shigeyuki Oba;Kikuya Kato;Shin Ishii
Affiliations:
Nara Institute of Science and Technology, Nara, Japan;Nara Institute of Science and Technology, Nara, Japan;Research Institute, Osaka Medical Center for Cancer and Cardiovascular Diseases, Osaka, Japan;Nara Institute of Science and Technology, Nara, Japan
Venue:
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
Year:
2005

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Abstract

We propose a novel probabilistic model for constructing a multi-class pattern classifier by weighted aggregation of general binary classifiers including one-versus-the-rest, one-versus-one, and others. Our model has a latent variable that represents class membership probabilities, and it is estimated by fitting it to probability estimate outputs of binary classfiers. We apply our method to classification problems of synthetic datasets and a real world dataset of gene expression profiles. We show that our method achieves comparable performance to conventional voting heuristics.