Quadratic Kernel-Free Least Square Twin Support Vector Machine for Binary Classification Problems

Abstract

In this paper, a new quadratic kernel-free least square twin support vector machine (QLSTSVM) is proposed for binary classification problems. The advantage of QLSTSVM is that there is no need to select the kernel function and related parameters for nonlinear classification problems. After using consensus technique, we adopt alternating direction method of multipliers to solve the reformulated consensus QLSTSVM directly. To reduce CPU time, the Karush-Kuhn-Tucker (KKT) conditions is also used to solve the QLSTSVM. The performance of QLSTSVM is tested on two artificial datasets and several University of California Irvine (UCI) benchmark datasets. Numerical results indicate that the QLSTSVM may outperform several existing methods for solving twin support vector machine with Gaussian kernel in terms of the classification accuracy and operation time.