Fuzzy support vector machines based on linear clustering

Abstract

A new Fuzzy Support Vector Machines (FSVMs) based on linear clustering is proposed in this paper. Its concept comes from the idea of linear clustering, selecting the data points near to the preformed hyperplane, which is formed on the training set including one positive and one negative training samples respectively. The more important samples near to the preformed hyperplane are selected by linear clustering technique, and the new FSVMs are formed on the more important data set. It integrates the merit of two kinds of FSVMs. The membership functions are defined using the relative distance between the data points and the preformed hyperplane during the training process. The fuzzy membership decision functions of multi-class FSVMs adopt the minimal value of all the decision functions of two-class FSVMs. To demonstrate the superiority of our methods, the benchmark data sets of machines learning databases are selected to verify the proposed FSVMs. The experimental results indicate that the proposed FSVMs can reduce the training data and running time, and its recognition rate is greater than or equal to that of FSVMs through selecting a suitable linear clustering parameter.