The SVM Classifier Based on the Modified Particle Swarm Optimization

被引：1
|
作者：
Demidova, Liliya [1 ]
Nikulchev, Evgeny [2 ]
Sokolova, Yulia [3 ]
机构：
[1] Ryazan State Radio Engn Univ, Moscow Technol Inst, Moscow, Russia
[2] Moscow Technol Inst, Moscow, Russia
[3] Ryazan State Radio Engn Univ, Ryazan, Russia
来源：
INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS | 2016年 / 7卷 / 02期
关键词：
particle swarm optimization; SVM-classifier; kernel function type; kernel function parameters; regularization parameter; support vectors;
D O I：
暂无
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
The problem of development of the SVM classifier based on the modified particle swarm optimization has been considered. This algorithm carries out the simultaneous search of the kernel function type, values of the kernel function parameters and value of the regularization parameter for the SVM classifier. Such SVM classifier provides the high quality of data classification. The idea of particles' "regeneration" is put on the basis of the modified particle swarm optimization algorithm. At the realization of this idea, some particles change their kernel function type to the one which corresponds to the particle with the best value of the classification accuracy. The offered particle swarm optimization algorithm allows reducing the time expenditures for development of the SVM classifier. The results of experimental studies confirm the efficiency of this algorithm.
引用
收藏
页码：16 / 24
页数：9
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