Interval-valued Fuzzy Support Vector Machine

Abstract

Since the traditional fuzzy support vector machine (FSVM) hardly distinguishes between the sparse sample points and the dense ones with the same membership, it may further reduce the classification accuracy. In order to solve this problem, by using the fuzzy support vector machine and interval-valued fuzzy set, the interval-valued fuzzy support vector machine is constructed. We call it interval-valued fuzzy SVM (IVFSVM).The simulation experiment shows that the classification result by using the IVFSVM is more accurate than the traditional SVM and the FSVM.