一种基于分类精度的特征选择支持向量机

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。

A kind of feature selection based on classification accuracy of SVM

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.