Abstract
Feature selection can effectively reduce the number of features and improve the accuracy of classification, so that reducing the computational burden and improving the performance of machine learning. In this paper, we propose an improved binary grey wolf optimization (IBGWO) algorithm for a wrapper-based feature selection method. Aiming at the shortcomings of grey wolf optimization (GWO) for feature selection, we first propose a enhanced opposition-based learning (E-OBL) initialization method to enhance the performance of initial solutions. Second, a local search strategy is introduced to balance the exploitation and exploration abilities of the IBGWO. Finally, a novel update mechanism is proposed for improving the population diversity and exploration capability of the algorithm. Simulations are conducted by using 16 well-known datasets, and the results show that the proposed method outperforms other benchmark algorithms on 12 datasets, and the introduced improved factors are suitable and effective.
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Acknowledgment
This work is supported in part by the National Key Research and Development Program of China (2018YFC0831706), in part by the National Natural Science Foundation of China (62002133, 61872158, 61806083), in part by the Science and Technology Development Plan Project of Jilin Province (20200201166JC, 20190701019GH, 20190701002GH), in part by Youth Science and Technology Talent Lift Project of Jilin Province (QT202013), and in part by the Excellent Young Talents Program for Department of Science and Technology of Jilin Province (Grant 20190103051JH).
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Li, W., Kang, H., Feng, T., Li, J., Yue, Z., Sun, G. (2021). Swarm Intelligence-Based Feature Selection: An Improved Binary Grey Wolf Optimization Method. In: Qiu, H., Zhang, C., Fei, Z., Qiu, M., Kung, SY. (eds) Knowledge Science, Engineering and Management. KSEM 2021. Lecture Notes in Computer Science(), vol 12817. Springer, Cham. https://doi.org/10.1007/978-3-030-82153-1_9
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