Multi-category classification by soft-max combination of binary classifiers

Authors:
Kaibo Duan;S. Sathiya Keerthi;Wei Chu;Shirish Krishnaj Shevade;Aun Neow Poo
Affiliations:
Control Division, Department of Mechanical Engineering, National University of Singapore, Singapore;Control Division, Department of Mechanical Engineering, National University of Singapore, Singapore;Control Division, Department of Mechanical Engineering, National University of Singapore, Singapore;Department of Computer Science and Automation, Indian Institute of Science, Bangalore;Control Division, Department of Mechanical Engineering, National University of Singapore, Singapore
Venue:
MCS'03 Proceedings of the 4th international conference on Multiple classifier systems
Year:
2003

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Abstract

In this paper, we propose a multi-category classification method that combines binary classifiers through soft-max function. Posteriori probabilities are also obtained. Both, one-versus-all and one-versus-one classifiers can be used in the combination. Empirical comparison shows that the proposed method is competitive with other implementations of one-versus-all and one-versus-one methods in terms of both classification accuracy and posteriori probability estimate.