Abstract
This paper proposes a new wrapper feature selection method based on the modified adaptive Human Learning Optimization (MAHLO) algorithm and Support Vector Machine (SVM). To achieve better results, the initialization and random learning strategies are modified in MAHLO to solve the feature selection problem more efficiently and the adaptive strategies are used to enhance the performance and relieve the effort of parameter setting. Besides, a two-stage evaluation function is adopted to eliminate the useless and redundant features, which is easier to operate in applications. The simulation results indicate that MAHLO can solve high-dimensional feature selection problems more efficiently, and the classification results on the UCI benchmark problems further demonstrate the efficiency and the advantage of the proposed MAHLO-based wrapper feature selection method.
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Acknowledgments
This work is supported by National Key Research and Development Program of China (No. 2019YFB1405500), National Natural Science Foundation of China (Grant No. 92067105 & 61833011), Key Project of Science and Technology Commission of Shanghai Municipality under Grant No. 19510750300 & 19500712300, and 111 Project under Grant No. D18003.
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Yu, S., Jia, Y., Hu, X., Ni, H., Wang, L. (2021). Feature Selection Based on a Modified Adaptive Human Learning Optimization Algorithm. In: Han, Q., McLoone, S., Peng, C., Zhang, B. (eds) Intelligent Equipment, Robots, and Vehicles. LSMS ICSEE 2021 2021. Communications in Computer and Information Science, vol 1469. Springer, Singapore. https://doi.org/10.1007/978-981-16-7213-2_76
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DOI: https://doi.org/10.1007/978-981-16-7213-2_76
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