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International Journal of Machine Learning and Cybernetics
Published in: International Journal of Machine Learning and Cybernetics 6/2017

10-06-2016 | Original Article

Multiple birth least squares support vector machine for multi-class classification

Authors: Su-Gen Chen, Xiao-Jun Wu

Published in: International Journal of Machine Learning and Cybernetics | Issue 6/2017

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Abstract

Least squares twin support vector machine (LSTSVM) was initially designed for binary classification. However, practical problems often require the discrimination more than two categories. To tackle multi-class classification problem, a novel algorithm, called multiple birth least squares support vector machine (MBLSSVM), is proposed. Our MBLSSVM solves K quadratic programming problems (QPPs) to obtain K hyperplanes, each problem is similar to binary LSTSVM. Comparison against the Multi-LSTSVM, Multi-TWSVM, MBSVM and our MBLSSVM on both UCI datasets and ORL, YALE face datasets illustrates the effectiveness of the proposed method.
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Metadata
Title
Multiple birth least squares support vector machine for multi-class classification
Authors
Su-Gen Chen
Xiao-Jun Wu
Publication date
10-06-2016
Publisher
Springer Berlin Heidelberg
Published in
International Journal of Machine Learning and Cybernetics / Issue 6/2017
Print ISSN: 1868-8071
Electronic ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-016-0554-7

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