关键词: 特征选择, 最小二乘支持向量机, 核极化

Abstract: In order to study each feature difference of sample in the Least Squares Support Vector Machine （LSSVM）, Gaussian kernel with multiple parameters is introduced. Through analyzing the geometric significance of kernel polarization, a feature selection algorithm based on kernel polarization gradient iteration optimization of multiple parameters Gaussian kernel is proposed. Firstly, by using the gradient iteration of kernel polarization algorithm, the important degree of each feature in the sample is measured. Then, the feature selection of LSSVM is selected by the importance of feature characteristics. Finally, the selected features in the LSSVM is trained and tested. The name of the method is called KP_LSSVM. The experimental results on the UCI dataset show that the proposed method can obtain more accurate classification results than the PCA_LSSVM, KPCA_LSSVM and LSSVM methods, and verify the effectiveness of the proposed method.

Key words: feature selection, Least Squares Support Vector Machine（LSSVM）, kernel polarization