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A Novel Twin Support Vector Machine for Binary Classification Problems

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Abstract

Based on the recently proposed twin support vector machine and twin bounded support vector machine, in this paper, we propose a novel twin support vector machine (NTSVM) for binary classification problems. The significance of our proposed NTSVM is that the objective function is changed in the spirit of regression, such that hyperplanes separate as much as possible. In addition, the successive overrelaxation technique is used to solve quadratic programming problems to speed up the training process. Experimental results obtained on several artificial and UCI benchmark datasets show the feasibility and effectiveness of the proposed method.

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Acknowledgments

This work is supported in part by the National Natural Science Foundation of China under grant No. 61373055 and No. 61103128. The authors would like to thank Dr. Yuan-Hai Shao from Zhejiang University of Technology for his valuable discussion and help.

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Authors and Affiliations

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

    Sugen Chen, Xiaojun Wu & Renfeng Zhang

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

    Sugen Chen

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  1. Sugen Chen
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  2. Xiaojun Wu
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  3. Renfeng Zhang
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Correspondence to Xiaojun Wu.

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Chen, S., Wu, X. & Zhang, R. A Novel Twin Support Vector Machine for Binary Classification Problems. Neural Process Lett 44, 795–811 (2016). https://doi.org/10.1007/s11063-016-9495-0

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  • DOI: https://doi.org/10.1007/s11063-016-9495-0

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