Self-Universum support vector machine

Abstract

In this paper, for an improved twin support vector machine (TWSVM), we give it a theoretical explanation based on the concept of Universum and then name it Self-Universum support vector machine (SUSVM). For the binary classification problem, SUSVM takes the positive class and negative class as Universum separately to construct two classification problems with Universum; therefore, two nonparallel hyperplanes are derived. SUSVM has several improved advantages compared with TWSVMs. Furthermore, we improve SUSVM by formulating it as a pair of linear programming problems instead of quadratic programming problems (QPPs), which leads to the better generalization performance and less computational time. The effectiveness of the enhanced method is demonstrated by experimental results on several benchmark datasets.