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Conference Papers Year : 2020

Feature Selection Algorithm Based on Multi Strategy Grey Wolf Optimizer

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Guangyue Zhou
  • Function : Author
  • PersonId : 1118837
Kewen Li
  • Function : Author
  • PersonId : 1118838
Guoqiang Wan
  • Function : Author
  • PersonId : 1118839
Hongtu Ji
  • Function : Author
  • PersonId : 1118840

Abstract

Feature selection is an important part of data mining, image recognition and other fields. The efficiency and accuracy of classification algorithm can be improved by selecting the best feature subset. The classical feature selection technology has some limitations, and heuristic optimization algorithm for feature selection is an alternative method to solve these limitations and find the optimal solution. In this paper, we proposed a Multi Strategy Grey Wolf Optimizer algorithm (MSGWO) based on random guidance, local search and subgroup cooperation strategies for feature selection, which solves the problem that the traditional grey wolf optimizer algorithm (GWO) is easy to fall into local optimization with a single search strategy. Among them, the random guidance strategy can make full use of the random characteristics to enhance the global search ability of the population, and the local search strategy makes grey wolf individuals make full use of the search space around the current best solution, and the subgroup cooperation strategy is very important to balance the global search and local search of the algorithm in the iterative process. MSGWO algorithm cooperates with each other in three strategies to update the location of grey wolf individuals, and enhances the global and local search ability of grey wolf individuals. Experimental results show that MSGWO can quickly find the optimal feature combination and effectively improve the performance of the classification model.
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Dates and versions

hal-03456986 , version 1 (30-11-2021)

Licence

Attribution - CC BY 4.0

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Guangyue Zhou, Kewen Li, Guoqiang Wan, Hongtu Ji. Feature Selection Algorithm Based on Multi Strategy Grey Wolf Optimizer. 11th International Conference on Intelligent Information Processing (IIP), Jul 2020, Hangzhou, China. pp.35-45, ⟨10.1007/978-3-030-46931-3_4⟩. ⟨hal-03456986⟩
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