关键词: 支持向量机, 人工鱼群算法, 参数优化, 遗传算法

Abstract: As considering that the parameter optimization of support vector machine lacks theory support and the SVM cross-validation method spends lots of time on selecting parameters, the parameter optimization selection method of support vector machine is proposed based on artificial fish-swarm algorithm. This method puts the SVM classification prediction accuracy rate as the optimization principle and uses the better parallelism of artificial fish-swarm algorithm and the stronger global optimization ability to achieve the optimal target and obtain optimal parameter combination of SVM. The results of numerical value experiments show that the artificial fish-swarm algorithm has faster performance optimization and higher classification accuracy rate in SVM parameters’ optimization selection. This method has the better parallelism and the stronger global optimization ability.

Key words: support vector machine, artificial fish-swarm algorithm, parameter optimization, genetic algorithm