Improved twin support vector machine

Abstract

We improve the twin support vector machine (TWSVM) to be a novel nonparallel hyperplanes classifier, termed as ITSVM (improved twin support vector machine), for binary classification. By introducing the different Lagrangian functions for the primal problems in the TWSVM, we get an improved dual formulation of TWSVM, then the resulted ITSVM algorithm overcomes the common drawbacks in the TWSVMs and inherits the essence of the standard SVMs. Firstly, ITSVM does not need to compute the large inverse matrices before training which is inevitable for the TWSVMs. Secondly, different from the TWSVMs, kernel trick can be applied directly to ITSVM for the nonlinear case, therefore nonlinear ITSVM is superior to nonlinear TWSVM theoretically. Thirdly, ITSVM can be solved efficiently by the successive overrelaxation (SOR) technique or sequential minimization optimization (SMO) method, which makes it more suitable for large scale problems. We also prove that the standard SVM is the special case of ITSVM. Experimental results show the efficiency of our method in both computation time and classification accuracy.