[1]SHI Songhui,DING Shifei.Energy-based structural least square twin support vector machine[J].CAAI Transactions on Intelligent Systems,2020,15(5):1013-1019.[doi:10.11992/tis.201906030]
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Energy-based structural least square twin support vector machine

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 15 Number of periods: 2020 5 Page number: 1013-1019 Column: 吴文俊人工智能科学技术奖论坛 Public date: 2020-09-05
Title:
Energy-based structural least square twin support vector machine
Author(s):
SHI Songhui1; 2;  DING Shifei1; 2
1. School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China;
2. Engineering Research Center (Ministry of Education) of Mine Digitization, Xuzhou 221116, China
Keywords:
twin support vector machine; least square; structural information; cluster; covariance matrix; energy factor; “all-versus-one” strategy; multi-class classification problem
CLC:
TP391
DOI:
10.11992/tis.201906030
Abstract:
The least square twin support vector machine (TWSVM) is very sensitive to noise and outlier. To solve this problem, we propose an energy-based structured least square TWSVM (ES-LSTWSVM). First, we perform a cluster analysis on each class, then compute the covariance matrix of each cluster in the classes, and introduce it into the objective function. To reduce the influence of noises and outliers on the algorithm, an energy factor is introduced to each hyperplane, and the equality constraint is converted into an energy-based form on the basis of least squares. Finally, we adopt the “all-versus-one” strategy to apply the proposed algorithm in solving a multi-class classification problem. The experimental results show that ES-LSTWSVM has good classification performance.
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