Fuzzy SVM Based on Vague Sets
In order to classify data with noises or outliers,Fuzzy support vector machine (FSVM) improve the generalization power of traditional SVM by assigning a fuzzy membership to each input data point. In this paper,an improved FSVM based on vague sets is proposed by assigning a truth-membership and a false-membership to each data point. And we reformulate the improved FSVM so that different input points can make different contributions to decision hyperplane. Furthermore,the key part of FSVM based on vague sets is how to construct the vague membership in the training data set. Fuzzy C-means clustering algorithm is employed for generating the truth-membership and false-membership. The experimental result shows the proposed model is promising.