关键词: 特征选择, 主成分分析, 支持向量机

Abstract: How to decrease the time of training and testing the samples，and improve the classification accuracy are important aspects of the feature selection research.A new feature selection approach by PCA and ReliefF is presented in this paper.The algorithm can take out the most representative features which constitute the effective feature sets from the original features，thus the dimensions of the features are decreased.Moreover the algorithm is proven to be more advantageous than the approach of PCA-GA in its simplicity and speed.Experiments on a UCI dataset show that the method in this paper provides a new research approach for information feature compression in pattern recognition.

Key words: feature selection, principle component analysis, support vector machine