[1]魏峻. 一种有效的支持向量机参数优化算法[J].计算机技术与发展,2015,25(12):97-100.
　WEI Jun. An Effective Parameter Optimization Algorithm of Support Vector Machine[J].,2015,25(12):97-100.
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 一种有效的支持向量机参数优化算法()

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
25
期数:
2015年12期
页码:
97-100
栏目:
智能、算法、系统工程
出版日期:
2015-12-10

文章信息/Info

Title:
 An Effective Parameter Optimization Algorithm of Support Vector Machine
文章编号:
1673-629X（2015)12-0097-04
作者:
 魏峻
 陕西理工学院 数学与计算机科学学院
Author(s):
 WEI Jun
关键词:
 ; 支持向量机; 参数选择; 蝙蝠算法; 核函数; ReliefF算法
Keywords:
 ; Support Vector Machine ( SVM); parameter selection; Bat Algorithm ( BA); kernel function; ReliefF algorithm
分类号:
TP18
文献标志码:
A
摘要:
 支持向量机是一种基于统计学习理论的采用结构风险最小化原则的机器学习方法,它的参数决定其性能的高低,因此正确选择相关参数显得非常重要. 文中将对支持向量机的惩罚参数及核参数进行优化. 蝙蝠算法是一种新型群智能算法,它具有模型简单、全局搜索能力强等特点. 文中提出基于ReliefF和蝙蝠算法( Ba)的支持向量机参数优化算法,对SVM的惩罚参数和核参数进行优化. 首先,基于ReliefF算法的基因初选,剔除与分类无关的噪声和冗余基因. 其次,进行基于Ba的SVM参数优化. 通过两个公共微阵列数据集的Matlab仿真实验,结果表明,文中算法搜索到的SVM最优参数能大幅提高支持向量机的性能,且具有较好的稳定性,是一种有效的支持向量机参数优化算法.
Abstract:
 Support Vector Machine ( SVM) is a kind of machine learning method based on statistical learning theory using structural risk minimization principle,the performance of SVM depends on the correct selection of related parameters. In this paper,the parameters of SVM are optimized. The Bat Algorithm ( BA) is a new type of swarm intelligence algorithm,which has a simple model,the strong search ability,etc. In this paper,propose SVM parameter optimization algorithm based on ReliefF and BA to optimize parameters of SVM. First of all,based on the genetic primaries of ReliefF algorithm,eliminate noise and redundant genes are not related with classification. Second-ly,SVM parameter optimization based on BA is carried out. Through two simulation experiments on public microarray data set,the ob-tained results show that the algorithm optimal parameters can greatly improve the performance of SVM,and has good stability,which is an effective algorithm of SVM parameters optimization.

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更新日期/Last Update: 2016-01-29