RBF-SVM feature selection arithmeticbased on kernel space mean inter-class distance
基于核空间类间平均距的径向基函数—支持向量机特征选择算法

The feature selection arithmetic of SVM-RFE is complex, and it always costs much time to select feature. This paper put forward an arithmetic based on RBF-SVM which select feature through kernel space mean inter-class distance to save feature selecting time. Firstly, this paper analysed the relation between the kernel parameter and kernel space mean inter-class distance. Then, it put forward the arithmetic which order the features through every feature's contribution to the kernel space mean inter-class distance. Finally, it made feaure selection experiments with eight subsets of UCI. The results of experiments indicates that the arithmetic of this paper is right and usefull, and it costs less time than SVM-RFE.