On efficient selection of binary classifiers for min-max modular classifier

Binary classifiers are fundamental components of multiclass pattern classifiers. How to construct a solution to a multiclass problem by efficiently combining the outputs of binary classifiers is a very important issue in neural network and machine learning research. In this paper, we present three different algorithms for selecting binary classifiers for min-max modular classifier to improve its response performance. We also give a theoretical performance estimation of the proposed algorithms. We prove that quadratic complexity of original min-max combination can be reduced to the level of linear complexity in the number of binary classifiers. The experimental results indicate that our proposed algorithms are efficient and effective.