Quadratic Kernel-Free Least Square Twin Support Vector Machine for Binary Classification Problems

doi:10.1007/s40305-018-00239-4

Journal of the Operations Research Society of China ›› 2019, Vol. 7 ›› Issue (4): 539-559.doi: 10.1007/s40305-018-00239-4

Special Issue: Continuous Optimization

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Quadratic Kernel-Free Least Square Twin Support Vector Machine for Binary Classification Problems

Qian-Qian Gao, Yan-Qin Bai, Ya-Ru Zhan

Department of Mathematics, Shanghai University, Shanghai, China

Received:2018-04-11 Revised:2018-10-21 Online:2019-11-30 Published:2019-11-28
Contact: Yan-Qin Bai, Qian-Qian Gao, Ya-Ru Zhan E-mail:yqbai@t.shu.edu.cn;1430827947@qq.com;zyr512054209@126.com
Supported by:
This research was supported by the National Natural Science Foundation of China (No. 11771275).

Abstract

Abstract: In this paper, a new quadratic kernel-free least square twin support vector machine (QLSTSVM) is proposed for binary classification problems. The advantage of QLSTSVM is that there is no need to select the kernel function and related parameters for nonlinear classification problems. After using consensus technique, we adopt alternating direction method of multipliers to solve the reformulated consensus QLSTSVM directly. To reduce CPU time, the Karush-Kuhn-Tucker (KKT) conditions is also used to solve the QLSTSVM. The performance of QLSTSVM is tested on two artificial datasets andseveral Universityof CaliforniaIrvine(UCI) benchmarkdatasets. Numerical results indicate that the QLSTSVM may outperform several existing methods for solving twin support vector machine with Gaussian kernel in terms of the classification accuracy and operation time.

Key words: Twin support vector machine, Quadratic kernel-free, Least square, Binary classification

CLC Number:

68T99
90C20

Qian-Qian Gao, Yan-Qin Bai, Ya-Ru Zhan. Quadratic Kernel-Free Least Square Twin Support Vector Machine for Binary Classification Problems[J]. Journal of the Operations Research Society of China, 2019, 7(4): 539-559.

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Quadratic Kernel-Free Least Square Twin Support Vector Machine for Binary Classification Problems

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