A Fuzzy Support Vector Machine Based on New Membership Function
摘要
Membership functions of traditional Fuzzy Support Vector Machine(FSVM) are mostly designed based on the distance between the samples and the class centers,which decrease the effect of support vectors.This paper combines two membership functions and presents a new membership function to solve this problem.The new membership function divides the samples into three parts:support vectors,non-support vectors and outliers.It assigns large membership values to the support vectors which are far away from their class center.Small membership values are assigned to non-support vectors and outliers which are also far away from their class center.Experimental results show that the FSVM with the proposed membership function is more effective in classification.