Support vector machine feature selection algorithm based on modified genetic algorithm

doi:10.3778/j.issn.1002-8331.2009.29.008

Computer Engineering and Applications ›› 2009, Vol. 45 ›› Issue (29): 28-30.DOI: 10.3778/j.issn.1002-8331.2009.29.008

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Support vector machine feature selection algorithm based on modified genetic algorithm

DU Zhuo-ming¹，FENG Jing²

1.School of Information Science and Technology，Northwest University，Xi’an 710127，China
2.College of Computer，Wuhan University of Technology，Wuhan 430064，China

Received:2009-06-08 Revised:2009-08-25 Online:2009-10-11 Published:2009-10-11
Contact: DU Zhuo-ming

改进遗传算法和支持向量机的特征选择算法

杜卓明¹，冯静²

1.西北大学信息科学与技术学院，西安 710127
2.武汉理工大学计算机学院，武汉 430064

通讯作者: 杜卓明

Abstract

Abstract: A novel Support Vector Machine（SVM） feature selection algorithm is proposed based on modified genetic algorithm.The recognition rate of SVM is used as separability criterion，and the crossover and mutation operation of traditional Genetic Algorithm（GA） are modified in the proposed algorithm.Two parameters of SVM are optimized during feature selecting.The results of experiment show that，the novel algorithm losses the recognition rates of 2.7 percent，but the length of feature vector is one fifth of traditional ones，and simplifies the complexity of classifier.

Key words: feature selection, support vector machine, genetic algorithm

摘要： 提出了一种基于改进遗传算法的特征选择算法。该算法以支持向量机分类器的识别率作为特征选择的可分性判据，对传统遗传算法的交叉和选择操作进行了改进，实现了指定数目的特征选择。而且算法在特征选择的过程中，还同时优化了支持向量机分类器的两个参数。实验数据的特征选择实验表明，提出的算法仅以损失2.7%识别率的代价，得到的特征维数却是传统遗传算法的1/5，极大地简化了分类器设计的复杂度。

关键词: 特征选择, 支持向量机, 遗传算法

CLC Number:

TP75

DU Zhuo-ming¹，FENG Jing². Support vector machine feature selection algorithm based on modified genetic algorithm[J]. Computer Engineering and Applications, 2009, 45(29): 28-30.

杜卓明¹，冯静². 改进遗传算法和支持向量机的特征选择算法[J]. 计算机工程与应用, 2009, 45(29): 28-30.

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Support vector machine feature selection algorithm based on modified genetic algorithm

改进遗传算法和支持向量机的特征选择算法

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