| 一种基于分类精度的特征选择支持向量机 |
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| 引用本文： | 易超群,李建平,朱成文. 一种基于分类精度的特征选择支持向量机[J]. 山东大学学报(理学版), 2010, 45(7): 119-121 |
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| 作者姓名： | 易超群 李建平 朱成文 |
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| 作者单位： | 国防科学技术大学理学院, 湖南 长沙 410073 |
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| 摘 要： | 在综合序列前向选择(sequential forward selection,SFS)方法和广义序列前向选择(generalized sequential forward selection,GSFS)方法的基础上,提出了基于分类精度的特征选取(sequential forward selection based on classification accuracy, CA-SFS)方法。它依次改变GSFS方法中的r值,并以支持向量机(support vector machine,SVM)作为分类器,将得出的分类精度作为准则函数对特征进行取舍。仿真实验表明CA-SFS算法不但选择了较少的特征,而且取得了较好的分类效果。
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| 关 键 词： | 特征选择 支持向量机 分类精度 仿真, |
| 收稿时间： | 2010-04-02 |
A kind of feature selection based on classification accuracy of SVM |
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| YI Chao-qun,LI Jian-ping,ZHU Cheng-wen. A kind of feature selection based on classification accuracy of SVM[J]. Journal of Shandong University, 2010, 45(7): 119-121 |
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| Authors: | YI Chao-qun LI Jian-ping ZHU Cheng-wen |
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| Affiliation: | College of Science, National University of Defense Technology, Changsha 410073, Hunan, China |
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| Abstract: | The sequential forward selection based on classification accuracy (CA-SFS) was proposed by associating sequential forward selection (SFS) with generalized sequential forward selection (GSFS). It varied the value of r in GSFS and employs SVM (support vector machine)as the classifier. The classification accuracy was taken as a criterion to decide the retention or elimination of features. Simulations showed that CA-SFS performed well both in selecting fewer features and classifying samples. |
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| Keywords: | feature selection support vector machine classification accuracy simulation |
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