Binary grey wolf optimizer with a novel population adaptation strategy for feature selection

Feature selection is a fundamental pre‐processing step in machine learning that aims to reduce the dimensionality of a dataset by selecting the most effective features from the original features. This process is regarded as a combinatorial optimization problem, and the grey wolf optimizer (GWO), a n...

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Bibliographic Details
Published in:IET control theory & applications 2023-11, Vol.17 (17), p.2313-2331
Main Authors: Wang, Dazhi, Ji, Yanjing, Wang, Hongfeng, Huang, Min
Format: Article
Language:English
Subjects:
Accuracy
Adaptation
Algorithms
Classification
Combinatorial analysis
Convergence
Data processing
dynamic mutation
Feature selection
Genetic algorithms
grey wolf optimizer
Heuristic
Heuristic methods
Machine learning
Methods
Mutation
Optimization
population adaptation strategy
Wolves
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Summary:Feature selection is a fundamental pre‐processing step in machine learning that aims to reduce the dimensionality of a dataset by selecting the most effective features from the original features. This process is regarded as a combinatorial optimization problem, and the grey wolf optimizer (GWO), a novel meta‐heuristic algorithm, has gained popularity in feature selection due to its fast convergence speed and easy implementation. In this paper, an improved binary GWO algorithm incorporating a novel Population Adaptation strategy called PA‐BGWO is proposed. The PA‐BGWO takes into account the characteristics of the feature selection problem and designs three strategies. The proposed strategy includes an adaptive individual update procedure to enhance the exploitation ability and accelerate convergence speed, a head wolf fine‐tuned mechanism to exert the impact on each independent feature of the objective function, and a filter‐based method ReliefF for calculating feature weights with dynamically adjusted mutation probabilities based on the ranking features to effectively escape from local optima. Experimental comparisons with several state‐of‐the‐art feature selection methods on 15 classification problems demonstrate that the proposed approach can select a small feature subset with higher classification accuracy in most cases.
ISSN:1751-8644
1751-8652
DOI:10.1049/cth2.12498