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A new fuzzy twin support vector machine for pattern classification

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Abstract

Fuzzy SVM is often used to solve the problem that patterns belonging to one class often play more significant roles in classification. In order to improve the efficiency and performance of fuzzy SVM, this paper proposes a new fuzzy twin support vector machine (NFTSVM) for binary classification, in which fuzzy neural networks and twin support vector machine (TWSVM) are incorporated. By design, the influence of the samples with high uncertainty can be mitigated by employing fuzzy membership to weigh the margin of each training sample, which improves the generalization ability. In addition, we show that the existing TWSVM and twin bounded support vector machines (TBSVM) are special cases of the proposed NFTSVM when the parameters of NFTSVM are appropriately selected. Moreover, the successive overrelaxation (SOR) technique is adopted to solve the quadratic programming problems (QPPs) in the proposed NFTSVM algorithm to speed up the training procedure. Experimental results obtained on several artificial and real-world datasets validate the feasibility and effectiveness of the proposed method.

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Acknowledgements

This work was partially supported by the National Natural Science Foundation of China (Grant No. 61373055 and 61672265), the University Natural Science Research Project of Anhui Province of China (Grant No. KJ2015A266, KJ2016A431 and KJ2017A361) and the University Outstanding Young Talent Support Project of Anhui Province of China (Grant No. gxyq2017026).

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Authors and Affiliations

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

    Su-Gen Chen & Xiao-Jun Wu

  2. School of Mathematics and Computational Science, Anqing Normal University, Anqing, 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. A new fuzzy twin support vector machine for pattern classification. Int. J. Mach. Learn. & Cyber. 9, 1553–1564 (2018). https://doi.org/10.1007/s13042-017-0664-x

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  • DOI: https://doi.org/10.1007/s13042-017-0664-x

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