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A novel projection twin support vector machine for binary classification

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Abstract

Based on the recently proposed projection twin support vector machine (PTSVM) and projection twin support vector machine with regularization term (RPTSVM), we propose a novel projection twin support vector machine (NPTSVM) for binary classification problems. Our proposed NPTSVM seeks two optimal projection directions simultaneously by solving a single quadratic programming problem, and the projected samples of one class are well separated from those of another class to some extent. Similar to RPTSVM, the singularity of matrix is avoided and the structural risk minimization principle is implemented in our NPTSVM. In addition, in our NPTSVM, we also discuss the nonlinear classification scenario which is not covered in PTSVM. The experimental results on several artificial and publicly available benchmark datasets show 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 Nos. 61373055, 61672265 and 61702012), the University Outstanding Young Talent Support Project of Anhui Province of China (Grant No. gxyq2017026) and the University Natural Science Research Project of Anhui Province of China (Grant Nos. KJ2016A431, KJ2017A361 and KJ2017A368).

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Authors and Affiliations

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

    Sugen Chen

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

    Xiaojun Wu & Hefeng Yin

  3. Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford, GU2 7XH, UK

    Hefeng Yin

Authors
  1. Sugen Chen
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  2. Xiaojun Wu
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  3. Hefeng Yin
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Correspondence to Xiaojun Wu.

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The authors declare that there is no conflict of interests regarding the publication of this paper.

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Communicated by V. Loia.

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Chen, S., Wu, X. & Yin, H. A novel projection twin support vector machine for binary classification. Soft Comput 23, 655–668 (2019). https://doi.org/10.1007/s00500-017-2974-z

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