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Indexed by:会议论文

Date of Publication:2015-06-14

Included Journals:EI、CPCI-S、Scopus

Volume:9243

Page Number:150-158

Key Words:Feature selection; Symmetrical uncertainty; Feature grouping; Genetic algorithm

Abstract:Feature selection technique has shown its power in analyzing the high dimensional data and building the efficient learning models. This study proposes a feature selection method based on feature grouping and genetic algorithm (FS-FGGA) to get a discriminative feature subset and reduce the irrelevant and redundancy data. Firstly, it eliminates the irrelevant features using the symmetrical uncertainty between features and class labels. Then, it groups the features by Approximate Markov blanket. Finally, genetic algorithm is applied to search the optimal feature subset from the different groups. Experiments on the eight public datasets demonstrate the effectiveness and superiority of FS-FGGA in comparison with SVM-RFE and ECBGS in most cases.