Algorithm of Fuzzy Support Vector Machine based on a Piecewise Linear Fuzzy Weight Method

This article describes how fuzzy support vector machines (FSVMs) function well with good anti-noise performance, which receives the attention of many experts. However, the traditional center-distance fuzzy weight assignment method assigns support vectors with a small value of a membership degree and this weakens the role of support vectors in classification. In this article, a piecewise linear fuzzy weight computing method is proposed, in which boundary samples are assigned with a larger value of membership degree and samples far from the mean vector are assigned a smaller value of membership degree. The proposed method has a good classification performance, because the influence of noise samples is weakened and meanwhile the support vectors are paid much more attention. The experiments on the UCI database and MNIST data set fully verify the effectiveness of the proposed algorithm.