[1]LI Jingcan,DING Shifei.Twin support vector machine based on artificial fish swarm algorithm[J].CAAI Transactions on Intelligent Systems,2019,14(6):1121-1126.[doi:10.11992/tis.201905025]
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Twin support vector machine based on artificial fish swarm algorithm

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 14 Number of periods: 2019 6 Page number: 1121-1126 Column: 学术论文—机器学习 Public date: 2019-11-05
Title:
Twin support vector machine based on artificial fish swarm algorithm
Author(s):
LI Jingcan1; 2;  DING Shifei1; 2
1. School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China;
2. Mine Digitization Engineering Research Center of Minstry of Education of the People’s Republic of China, Xuzhou 221116, China
Keywords:
twin support vector machine; artificial fish swarm algorithm; pattern classification; parameter optimization; accuracy; swarm intelligence; quadratic programming; parallel processing; global optimization
CLC:
TP391.4
DOI:
10.11992/tis.201905025
Abstract:
Twin support vector machine (TWSVM) is a machine learning algorithm based on the support vector machine. It has the advantages of fast training speed and superior classification performance. However, the algorithm cannot handle the parameter selection problem effectively, and the inappropriate parameters will reduce the classification ability. The artificial fish swarm algorithm (AFSA) is a group intelligent optimization algorithm with a strong global optimization ability and parallel processing capability. In this paper, TWSVM and AFSA are combined to solve the parameter selection problem of the TWSVM. First, the parameters of the support vector machine are taken as the position information of the artificial fish, and the classification accuracy is taken as the objective function. Then, the position and the optimal solution are updated by the artificial fish’s preying, swarming, following, and random behavior. At the end of the iterations, the optimal parameters and the optimal classification accuracy are obtained. The algorithm automatically determines the parameters of the TWSVM in the training process, avoiding the blindness of parameter selection, and improves the classification performance of the TWSVM
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