关键词: 支持向量机, 参数优化方法, 均匀设计, 网格搜索, 粒子群算法, 遗传算法

Abstract:

In practical applications, the performance of Support Vector Machine (SVM) depends on the selection of parameters. SVM parameter selection problems are studied and analyzed, and a parameter optimization method of SVM based on uniform design is proposed. Our method is compared with the parameter optimization methods of SVM based on grid search, particle swarm optimization, and genetic algorithms. Multiple classification data sets with different sizes are used for testing, so as to compare the classification accuracy and runtime of the four methods. Simulation results show that all the four methods can find the optimal parameters, which make the classification accuracy of SVM approach or exceed the theoretical accuracy of categorical datasets, and our proposed method has the characteristic of finding parameters in short time.

Key words: support vector machine;parameter optimization method;uniform design;grid search;particle swarm optimization;genetic algorithm