Fast clustering-based weighted twin support vector regression

Construction of an effective model for regression to fit data samples with noise or outlier is a challenging work. In this paper, in order to reduce the influence of noise or outlier on regression and further improve the prediction performance of standard twin support vector regression (TSVR), we proposed a fast clustering-based weighted TSVR, termed as FC-WTSVR. First, we use a fast clustering algorithm to quickly classify samples into different categories based on their similarities. Secondly, to reflect the prior structural information and distinguish contributions of samples located at different positions to regression, we introduce the covariance matrix and weighted diagonal matrix into the primal problems of FC-WTSVR, respectively. Finally, to shorten the training time, we adopt the successive over-relaxation algorithm to solve the quadratic programming problems. The results show that the proposed FC-WTSVR can obtain better prediction performance and anti-interference capability than some state-of-the-art algorithms.