Abstract：The min-max modular support vector machine (M3-SVM) is a powerful tool for dealing with large-scale data. To improve the classification performance of M3-SVM for unblanced data with high dimension, several random subspace strategies are analyzed and combined with M3-SVM to reduce the dimensionality and add the ensemble mechanism on feature level. Thus, the min-max modular support vector machine with random subspace is proposed. The experimental results on real-world datasets including unbalanced data indicate that the proposed random subspace strategy enhances the classification of M3-SVM. Moreover, the diversity between sub-modules (base learner) in M3-SVM is discussed.
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2014, Vol. 27 
