[1]QIAN Xiaoshan,YANG Chunhua.A parameter selection method of a least squares support vector machine based on gene expression programming[J].CAAI Transactions on Intelligent Systems,2012,7(3):225-229.
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A parameter selection method of a least squares support vector machine based on gene expression programming

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 7 Number of periods: 2012 3 Page number: 225-229 Column: 学术论文—人工智能基础 Public date: 2012-06-25
Title:
A parameter selection method of a least squares support vector machine based on gene expression programming
Author(s):
QIAN Xiaoshan1; 2;  YANG Chunhua1
1. School of Information Science and Engineering, Central South University, Changsha 410083, China;
2. Physical Science and Technology College, Yichun University, Yichun 336000, China
Keywords:
gene expression programming (GEP);  least squares support vector machine (LSSVM);  parameter selection;  particle swarm optimization (PSO);  genetic algorithm (GA)
CLC:
TP181
DOI:
-
Abstract:
To solve the multiparameter optimization problem of least squares support vector machines (LSSVM), a parameter optimization method based on gene expression programming (GEP) was proposed. The parameter (C,σ) samples of LSSVM were selected to be genes for GEP according to the mechanism of the dynamic change of the mutation operator with the gene number of the genome and the number of evolutionary generations. As a result, the convergence rate and accuracy were greatly increased. The new method was compared with other parameter optimization methods based on particle swarm optimization (PSO) and a genetic algorithm (GA) by several standard test functions, and the results show that the proposed method obtains the minimum fitting error. Finally, a parameter prediction model of the evaporation process of alumina production was established; the verification results using the industrial production data show that the method is effective and the result is satisfactory.
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