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A Twin Multi-Class Classification Support Vector Machine

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Abstract

Twin support vector machine (TSVM) is a novel machine learning algorithm, which aims at finding two nonparallel planes for each class. In order to do so, one needs to resolve a pair of smaller-sized quadratic programming problems rather than a single large one. Classical TSVM is proposed for the binary classification problem. However, multi-class classification problem is often met in our real world. For this problem, a new multi-class classification algorithm, called Twin-KSVC, is proposed in this paper. It takes the advantages of both TSVM and K-SVCR (support vector classification-regression machine for k-class classification) and evaluates all the training points into a “1-versus-1-versus-rest” structure, so it generates ternary outputs { −1, 0, +1}. As all the samples are utilized in constructing the classification hyper-plane, our proposed algorithm yields higher classification accuracy in comparison with other two algorithms. Experimental results on eleven benchmark datasets demonstrate the feasibility and validity of our proposed algorithm.

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Notes

  1. http://archive.ics. uci. edu/ml/datasets.html.

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Acknowledgments

The authors thank all anonymous referees for helpful comments that have leaded to improvement of the paper. This work was supported by National Natural Science Foundation of China (Grant No. 61153003, 11171346).

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Authors and Affiliations

  1. College of science, China Agricultural University, Beijing, 100083, China

    Yitian Xu, Rui Guo & Laisheng Wang

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  1. Yitian Xu
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  2. Rui Guo
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  3. Laisheng Wang
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Correspondence to Yitian Xu.

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Xu, Y., Guo, R. & Wang, L. A Twin Multi-Class Classification Support Vector Machine. Cogn Comput 5, 580–588 (2013). https://doi.org/10.1007/s12559-012-9179-7

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