Twin Support Vector Machines Based on Particle Swarm Optimization

Abstract

Twin support vector machines (TWSVM) is similar in spirit to proximal SVM based on generalized eigenvalues (GEPSVM), which constructs two nonparallel planes by solving two related SVM-type problems, so that its computing cost in the training phase is only 1/4 of standard SVM. In addition to keeping the advantages of GEPSVM, the classification performance of TWSVM is also significantly better than that of GEPSVM. However, there are also many deficiencies in TWSVM, difficult to specify the parameters is one of them, in order to overcome this deficiency, in this paper, we propose the twin support vector machines based on particle swarm optimization (PSO-TWSVM). This algorithm use PSO to find the parameters for TWSVM, so that blindly parameters selection is avoided. The experimental results show that this algorithm is able to find the suitable parameters, and has higher classification accuracy compared with some other algorithms.