Abstract
Feature selection is an important preprocessing technique of data, which can be generally modeled as a binary optimization problem. Brain storm optimization (BSO) is a newly proposed algorithm that has not been systematically applied to feature selection problems yet. This paper studies an effective wrapper feature selection method based on BSO. Focused on this goal, firstly, a selective probability-based real encoding strategy of individual is introduced to transform the binary feature selection problem into a continuous optimization one. Based on this, then a continuous BSO-based feature selection algorithm (CBSOFS) is proposed. The proposed algorithm is tested on standard benchmark datasets and then compared to four representative algorithms. Experimental results show that CBSOFS achieves comparable results with compared algorithms.
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Acknowledgement
This work was jointly supported by National Natural Science Foundation of China (No. 61473299, 61473298, 61573361), and Jiangsu Six Talents Peaks Project of Province under Grant No. DZXX-053.
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Zhang, Xt., Zhang, Y., Gao, Hr., He, Cl. (2018). A Wrapper Feature Selection Algorithm Based on Brain Storm Optimization. In: Qiao, J., et al. Bio-inspired Computing: Theories and Applications. BIC-TA 2018. Communications in Computer and Information Science, vol 952. Springer, Singapore. https://doi.org/10.1007/978-981-13-2829-9_28
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DOI: https://doi.org/10.1007/978-981-13-2829-9_28
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