Authors: Xiuxi Wei
Addresses: Information Engineering Department, Guangxi International Business Vocational College, Nanning 530007, China
Abstract: The recently proposed twin parametric insensitive support vector regression, denoted by TPISVR, gets perforce regression performance and is suitable for many cases, especially when the noise is heteroscedastic. However, in the TPISVR, it solves two dual quadratic programming problems (QPPs). Moreover, compared with support vector regression (SVR), TPISVR has at least four regularisation parameters that need regulating, which would affect its practical applications. In this paper, we increase the efficiency of TPISVR from two aspects. First, by introducing the least squares method, we propose a novel least squares twin parametric insensitive support vector regression, called LSTPISVR for short. LSTPISVR attempts to solve two modified primal problems of TPISVR, instead of two dual problems usually solved. Compared with the traditional solution method, LSTPISVR can improve the training speed without loss of generalisation. Second, a discrete binary particle swarm optimisation (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.
Keywords: support vector regression; SVR; twin support vector regression; least squares; twin parametric insensitive support vector regression; binary particle swarm optimisation; BPSO.
International Journal of Collaborative Intelligence, 2019 Vol.2 No.1, pp.51 - 65
Received: 03 Feb 2018
Accepted: 28 Mar 2018
Published online: 14 Mar 2019 *