Dual Membership Based Fuzzy Support Vector Machine Algorithm

A novel dual membership based fuzzy support vector machine (DM-FSVM) is presented while traditional fuzzy support vector machine (FSVM) is anal sized. There is only one membership in the samples of training sets of traditional SVM model, but in DM-FSVM, there are two memberships. The theoretically and simulate experiments show that this new method not only can keep the advantages of traditional FSVM, but also makes fully use of limited data and improves the classification efficiencies and the classification accuracy.