On Effective Decomposition of Training Data Sets for Min-Max Modular Classifier

Abstract

Our previous work shows that traditional randomization partition method for min-max modular (M) classifier can not ensure stable generalization accuracy when the number of two-class problems increases. To overcome this drawback, we consider how to effectively decompose the training data set for a two-class problem in this paper. We propose four basic… (More)

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