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Soft Computing
Published in: Soft Computing 8/2020

27-02-2020 | Foundations

Fast clustering-based weighted twin support vector regression

Authors: Binjie Gu, Jianwen Fang, Feng Pan, Zhonghu Bai

Published in: Soft Computing | Issue 8/2020

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Abstract

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.
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Metadata
Title
Fast clustering-based weighted twin support vector regression
Authors
Binjie Gu
Jianwen Fang
Feng Pan
Zhonghu Bai
Publication date
27-02-2020
Publisher
Springer Berlin Heidelberg
Published in
Soft Computing / Issue 8/2020
Print ISSN: 1432-7643
Electronic ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-020-04746-6

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