A heuristic weight-setting strategy and iteratively updating algorithm for weighted least-squares support vector regression

Abstract

Weighted least-squares support vector machine (WLS-SVM) is an improved version of least-squares support vector machine (LS-SVM). It adds weights on error variables to correct the biased estimation of LS-SVM. Traditional weight-setting algorithm for WLS-SVM depends on results from unweighted LS-SVM and requires retraining of WLS-SVM. In this paper, a heuristic weight-setting method is proposed. This method derives from the idea of outlier mining, and is independent of unweighted LS-SVM. More importantly, a fast iterative updating algorithm is presented, which reaches the final results of WLS-SVM through a few updating steps instead of directly retraining WLS-SVM. Circumstantial experiments on simulated instances and real-world datasets are conducted, demonstrating comparable results of the proposed WLS-SVM and encouraging performance of the fast iterative updating algorithm.