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.
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Acknowledgments
The study has been supported by the State Key Science & Technology Project for Infectious Diseases (2012ZX10002011), the Sino-German Center for Research Promotion (GZ 753), National Natural Science Foundation of China (21375011).
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Lin, X., Wang, X., Xiao, N., Huang, X., Wang, J. (2015). A Feature Selection Method Based on Feature Grouping and Genetic Algorithm. In: He, X., et al. Intelligence Science and Big Data Engineering. Big Data and Machine Learning Techniques. IScIDE 2015. Lecture Notes in Computer Science(), vol 9243. Springer, Cham. https://doi.org/10.1007/978-3-319-23862-3_15
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DOI: https://doi.org/10.1007/978-3-319-23862-3_15
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