A new algorithm of fuzzy support vector machine based on niche

Abstract

A new algorithm of fuzzy support vector machine based on niche is presented in this paper. In this algorithm, through comparing samples niche with class niche, the method of simply using Euclidean distance to measure the relationship of samples and class in the traditional support vector machine is changed by using the minimum radius in class niche, and the disadvantages of traditional support vector machine, which are sensitive to noise and outliers, and poor performance of differentiation of valid samples are overcome. Experimental data show that compared with the traditional support vector machine which only uses the distance between the sample and the center of class, this new algorithm can improve the convergence speed, and thus greatly enhance the discrimination between valid samples and noise samples.