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

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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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Acknowledgments

The authors are very grateful to the Editor, Professor Xi-Zhao Wang, and other anonymous reviewers for their helpful and valuable comments and suggestions which improved the quality of the paper. This work was supported in part by the National Natural Science Foundation of China under Grant No. 61373055 and No. 61103128.

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Authors and Affiliations

  1. School of IoT Engineering, Jiangnan University, Wuxi Jiangsu, 214122, People’s Republic of China

    Su-Gen Chen & Xiao-Jun Wu

  2. School of Mathematics and Computational Science, Anqing Normal University, Anqing Anhui, 246133, People’s Republic of China

    Su-Gen Chen

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  1. Su-Gen Chen
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  2. Xiao-Jun Wu
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Correspondence to Xiao-Jun Wu.

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Chen, SG., Wu, XJ. Multiple birth least squares support vector machine for multi-class classification. Int. J. Mach. Learn. & Cyber. 8, 1731–1742 (2017). https://doi.org/10.1007/s13042-016-0554-7

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  • DOI: https://doi.org/10.1007/s13042-016-0554-7

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