关键词: F-score, 支持向量机, 特征选择, 主成分分析, 核函数主成分分析

Abstract: The original F-score can only measure the discrimination of two sets of real numbers. This paper proposed the improved F-score which can not only measure the discrimination of two sets of real numbers, but also the discrimination of more than two sets of real numbers. The improved F-score and Support Vector Machines (SVM) were combined in this paper to accomplish the feature selection process where the improved F-score was used as the evaluation criterion of feature selection, and SVM to evaluate the features selected via the improved F-score. Experiments have been conducted on six different groups from UCI machine learning database. The experimental results show that the feature selection method, based on the improved F-score and SVM, has high classification accuracy and good generalization, and spends less training time than that of the Principle Component Analysis (PCA)+SVM method.

Key words: F-score, Support Vector Machine (SVM), feature selection, Principle Component Analysis （PCA）, Kernel Principal Component Analysis (KPCA)