Fuzzy twin support vector machine based on iterative method

ABSTRACT Twin support vector machine (TWSVM) is an important machine learning method, whose objective is to construct two nonparallel hyperplanes such that each hyperplane is closer to one of two classes and as far as possible from the other class. As TWSVM solves two smaller size quadratic programming problems (QPPs), it works faster than standard support vector machine. However, this method does not consider importance of different data sample, and solution of QPPs still uses the Lagrange multiplier method. In this paper, we study two fuzzy twin support vector machines, which are FTWSVM and v-FTWSVM respectively, using successive over-relaxation (SOR) iterative method. Experiments are conducted for both FTWSVM and v-FTWSVM on some UCI datasets. The results indicate that the speed and accuracy of using successive over-relaxation iterative method for fuzzy twin support vector machine are superior to those of using the traditional solution method