Abstract
Feature selection is a technique used in data pre-processing to select the most relevant subset of features from a larger set, with the goal of improving classification performance. Evolutionary algorithms have been commonly proposed to solve feature selection problems, but they can suffer from issues originated from diversity reduction and crowding distance decrease, which can lead to suboptimal results. In this study, we propose a new evolutionary algorithm called clustering strategy based evolutionary algorithm (CEA) for feature selection in classification. CEA combines the clustering mechanism to gather individuals into different clusters, and the crossover operation is dominated by the parents in different clusters, thus enhancing the exploration ability of the algorithm and avoiding the population falling into the local optimal solution space. The performance of CEA was evaluated on 13 classification datasets and compared to four mainstream evolutionary algorithms. The experimental results showed that CEA was able to achieve better classification performance using similar or fewer features than the other algorithms.
This research was partially supported by the Japan Society for the Promotion of Science (JSPS) KAKENHI under Grant JP22H03643, Japan Science and Technology Agency (JST) Support for Pioneering Research Initiated by the Next Generation (SPRING) under Grant JPMJSP2145, JST through the Establishment of University Fellowships towards the Creation of Science Technology Innovation under Grant JPMJFS2115, and Natural Science Foundation of Jiangsu Province (No. BK20210605).
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Zhang, B., Wang, Z., Lei, Z., Yu, J., Jin, T., Gao, S. (2023). A Clustering Strategy-Based Evolutionary Algorithm for Feature Selection in Classification. In: Fujita, H., Wang, Y., Xiao, Y., Moonis, A. (eds) Advances and Trends in Artificial Intelligence. Theory and Applications. IEA/AIE 2023. Lecture Notes in Computer Science(), vol 13925. Springer, Cham. https://doi.org/10.1007/978-3-031-36819-6_5
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