Fuzzy support vector machine based on non-equilibrium data

Abstract

Fuzzy support vector machine (FSVM), whose membership function is based on class centers, can effectively solve the problem that the traditional support vector machine (SVM) is sensitive to the noises and outliers. However, FSVM assigns smaller memberships to support vectors, which may decrease the effects of these support vectors upon the construction of classification hyperplane. At the same time, FSVM has some disadvantages in dealing with the non-equilibrium data classification. Therefore, a novel method to determine membership function is proposed, and a new FSVM based on non-equilibrium data is constructed. Experiments show that the new FSVM can effectively reduce the misclassification rates produced by the class with fewer samples in dealing with non-equilibrium data classification problem. Therefore, the proposed FSVM may make the misclassification rates upon two classes approximately equal.