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International Journal of Machine Learning and Cybernetics
Published in: International Journal of Machine Learning and Cybernetics 5/2015

01-10-2015 | Original Article

Interval twin support vector regression algorithm for interval input-output data

Authors: Xinjun Peng, Dongjing Chen, Lingyan Kong, Dong Xu

Published in: International Journal of Machine Learning and Cybernetics | Issue 5/2015

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Abstract

It is necessary to use interval data to define terms or describe extreme behaviors because of the existence of uncertainty in many real-world problems. In this paper, a novel efficient interval twin support vector regression (ITSVR) is proposed to handle such interval data. This ITSVR employs two nonparallel functions to identify the upper and lower sides of the interval output data, respectively, in which the Hausdorff distance is incorporated into the Gaussian kernel as the interval kernel for interval input data. Compared with other support vector regression (SVR)-based interval regression methods, such as the interval support vector interval regression networks (ISVIRN), this ITSVR algorithm is more efficient since only two smaller-sized QPPs are solved, respectively. The experimental results on several artificial datasets and three stock index datasets show the validity of ITSVR.
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Metadata
Title
Interval twin support vector regression algorithm for interval input-output data
Authors
Xinjun Peng
Dongjing Chen
Lingyan Kong
Dong Xu
Publication date
01-10-2015
Publisher
Springer Berlin Heidelberg
Published in
International Journal of Machine Learning and Cybernetics / Issue 5/2015
Print ISSN: 1868-8071
Electronic ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-015-0395-9

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