Improvement on response performance of min-max modular classifier by symmetric module selection

Authors:
Hai Zhao;Baoliang Lu
Affiliations:
Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China;Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China
Venue:
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
Year:
2005

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Abstract

This paper aims to improve the response performance of min-max modular classifier by a module selection policy for two-class classification during recognition. We propose an efficient base classifier selection algorithm. We show that the quadratic complexity of original min-max modular classifier can fall onto the level of linear complexity in the number of base-classifier modules for each input sample under presented selection scheme. The experimental results indicate the effectiveness of our algorithm and verify our theoretical analysis.