Affiliation:
1. Jiangxi Science and Technology Normal University
2. Zhejiang Great Wall Construction Supervising Limited Company
3. Beijing Institute of Technology
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.
Publisher
Trans Tech Publications, Ltd.
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