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International Journal of Machine Learning and Cybernetics
Published in: International Journal of Machine Learning and Cybernetics 1/2017

11-01-2015 | Original Article

A structural information-based twin-hypersphere support vector machine classifier

Authors: Xinjun Peng, Lingyan Kong, Dongjing Chen

Published in: International Journal of Machine Learning and Cybernetics | Issue 1/2017

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Abstract

Twin-hypersphere support vector machine (THSVM) for binary pattern recognition aims at generating two hyperspheres in the feature space such that each hypersphere contains as many as possible samples in one class and is as far as possible from the other one. THSVM has a fast learning speed since it solves two small sized support vector machine (SVM)-type quadratic programming problems (QPPs). However, it only simply considers the prior class-based structural information in the optimization problems. In this paper, a structural information-based THSVM (STHSVM) classifier for binary classification is presented. This proposed STHSVM focuses on the cluster-based structural information of the corresponding class in each optimization problem, which is vital for designing a good classifier in different real-world problems. In addition, it also leads to a fast learning speed since this STHSVM solves a series of smaller-sized QPPs compared with THSVM. Experimental results demonstrate that STHSVM is superior in generalization performance to other classifiers.
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Appendix
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Footnotes
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Metadata
Title
A structural information-based twin-hypersphere support vector machine classifier
Authors
Xinjun Peng
Lingyan Kong
Dongjing Chen
Publication date
11-01-2015
Publisher
Springer Berlin Heidelberg
Published in
International Journal of Machine Learning and Cybernetics / Issue 1/2017
Print ISSN: 1868-8071
Electronic ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-014-0323-4

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