A Hybrid Feature Selection Method Based on Fisher Score and Genetic Algorithm

Fisher score and genetic algorithm are widely used for feature selection. However, some redundant features will be selected by Fisher score, and the convergence properties may be worse if the initial population of genetic algorithm is generated by a random manner. To improve the performance of feature selection by Fisher score and genetic algorithm, we propose a hybrid feature selection method, which merging the advantages of Fisher score and genetic algorithm together. It aims at utilizing the features' Fisher score to generate the initial population of genetic algorithm. To begin with, the Fisher scores of all the 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 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