Imbalanced Data Classification Using Cost-Sensitive Support Vector Machine Based on Information Entropy

Abstract

As a supervised classification algorithm, Support Vector Machine (SVM) has an excellent ability in solving small samples, nonlinear and high dimensional classification problems. However, SVM is inefficient for imbalanced data sets classification. Therefore, a cost sensitive SVM (CSSVM) should be designed for imbalanced data sets classification. This paper proposes a method which constructed CSSVM based on information entropy, and in this method the information entropies of different classes of data set are used to determine the values of penalty factor of CSSVM.