A Novel Twin Support Vector Machine for Binary Classification Problems

Based on the recently proposed twin support vector machine and twin bounded support vector machine, in this paper, we propose a novel twin support vector machine (NTSVM) for binary classification problems. The significance of our proposed NTSVM is that the objective function is changed in the spirit of regression, such that hyperplanes separate as much as possible. In addition, the successive overrelaxation technique is used to solve quadratic programming problems to speed up the training process. Experimental results obtained on several artificial and UCI benchmark datasets show the feasibility and effectiveness of the proposed method.