Feature selection method combing improved F-score and support vector machine
Yan Zhang · Journal of Computer Applications · 2010
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.