关键词: 乌鸦搜索算法, 支持向量机, 参数优化

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.

Key words: crow search algorithm, support vector machine, parameters optimization