计算机工程与应用2019，Vol.55Issue(21)：213-218,238,7.DOI:10.3778/j.issn.1002-8331.1903-0381
乌鸦搜索算法在SVM参数优化中的应用
Application of Crow Search Algorithm in SVM Parameter Optimization
摘要
Abstract
The selection of parameters have a crucial impact on the classification accuracy and generalization capabilities of Support Vector Machine(SVM)and the swarm intelligence algorithm has been widely used in parameter optimization in recent years. In this context, the CSA-SVM model is proposed. The model uses the classification error rate as the objective function, and applies the Crow Search Algorithm(CSA)to obtain the optimal parameter combination of the SVM. In order to verify the classification performance of the CSA-SVM model, the model is applied to six standard classification data sets and compares with the performance of the Genetic Algorithm(GA)and Particle Swarm Optimization(PSO) algorithm respectively. The experimental results show that the CSA algorithm has better searching ability and faster searching speed in the SVM parameter selection, and CSA-SVM model has high classification accuracy.关键词
乌鸦搜索算法/支持向量机/参数优化Key words
crow search algorithm/support vector machine/parameters optimization分类
信息技术与安全科学引用本文复制引用
王丽婷,张金鑫,张金华..乌鸦搜索算法在SVM参数优化中的应用[J].计算机工程与应用,2019,55(21):213-218,238,7.基金项目
国家社会科学基金(No.15BGL206). （No.15BGL206）
