A Research Of Feature Selection Methods Based On Fisher Score And Genetic Algorithm

Feature selection is one of the popular researches in machine learning area. This article provides a brief review about the background of feature selection, and analyses the advantages and drawbacks of some feature selection methods. Due to the complementary of filter based and wrapper based feature selection algorithm, we propose a hybrid feature selection method. In the first place, the Fisher scores of all features will be mapped into a specific interval by a linear function, and then the rescaled Fisher scores will be utilized to generate the initial population of genetic algorithm. Finally, the initial population will be used in the subsequent procedure of genetic algorithm to perform feature selection with elitist strategy for reference. In this paper, we choose four data sets of Sonar,WDBC, Arrhythmia and Hepatitis to test the performance of our proposed algorithm. Feature subsets of the four data sets will be selected by our algorithm, and then the dimensionality of data sets will be reduced according to the selected feature subsets respectively. 1-NN classifier is used to classify the dimensionality reduced data sets, and respectively achieving the classification accuracy of 72.36%, 95.64%, 72.04% and 87.83% with ten-fold cross validation method. The experiment results show that, compared to the performance of Fisher Score(FS), Genetic Algorithm(GA) and Fisher Score Genetic Algorithm(FSGA), our algorithm is fit for eliminate redundant features, and it can select discriminative features. Above all, our method is effective in feature selection.