Abstract
In this era of data explosion, feature selection has received sustained attention to remove the large amounts of meaningless data and improve the classification ac-curacy rate. In this paper, a feature selection method based on reinforcement-behaved strategy is proposed, which identifies the most important features by embedding the Brain Storm Optimization (BSO) algorithm into the classifier. The ideas of the BSO are mapped to feature subsets, and the importance of the feature is evaluated through some indicators, i.e. the validity of the feature migration. In the migration of each feature, the feature is updated to a new feature in the same position between the two generations. The feedback of each action is used as the basis for the ordering of feature importance. An updating strategy is presented to modify the actions based on the current state to improve the feature set. The effectiveness of the proposed algorithm has been demonstrated on six different binary classification datasets (e.g., biometrics, geography, etc.) in comparison to several embedded methods. The results show that our proposed method is superior in high performance, stability and low computing costs.
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Acknowledgment
This work is partially supported by The National Natural Science Foundation of China (Grants Nos. 71571120, 71271140, 71471158, 71001072, 61472257), Natural Science Foundation of Guangdong Province (2016A030310074, 2018A030310575), Innovation and Entrepreneurship Research Center of Guangdong University Student (2018A073825), Shenzhen Science and Technology Plan (CXZZ20140418182638764), Research Foundation of Shenzhen University (85303/00000155), Research Cultivation Project from Shenzhen Institute of Information Technology (ZY201717), and Innovating and Upgrading Institute Project from Department of Education of Guangdong Province (2017GWTSCX038).
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Niu, B., Yang, X., Wang, H. (2019). Feature Selection Using a Reinforcement-Behaved Brain Storm Optimization. In: Huang, DS., Huang, ZK., Hussain, A. (eds) Intelligent Computing Methodologies. ICIC 2019. Lecture Notes in Computer Science(), vol 11645. Springer, Cham. https://doi.org/10.1007/978-3-030-26766-7_61
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DOI: https://doi.org/10.1007/978-3-030-26766-7_61
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