BPSO optimizing for least squares twin parametric insensitive support vector regression

Abstract

The recently proposed twin parametric insensitive support vector regression, denoted by TPISVR, which solves two dual quadratic programming problems (QPPs). However, TPISVR has at least four regularization parameters that need regulating. In this paper, we increase the efficiency of TPISVR from two aspects. Fist, we propose a novel least squares twin parametric insensitive support vector regression, called LSTPISVR for short. Compared with the traditional solution method, LSTPISVR can improve the training speed without loss of generalization. Second, a discrete binary particle swarm optimization (BPSO) algorithm is introduced to do the parameter selection. Computational results on several synthetic as well as benchmark datasets confirm the great improvements on the training process of our LSTPISVR.