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Feature selection using importance-based two-stage multi-modal multiobjective particle swarm optimization
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AI
DOI：10.1007/s10586-024-04807-7.png)
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Abstract
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Feature selection is aimed at reducing the dimensionality of datasets while maintaining or enhancing classification accuracy. Recent studies have increasingly approached feature selection through multi-modal, multi-objective optimization. However, for high-dimensional datasets, the large decision space limits the ability of traditional multi-modal, multi-objective optimization algorithms to effectively eliminate redundant features. To address this challenge, a two-stage multi-modal, multi-objective particle swarm optimization algorithm incorporating feature importance for feature selection is proposed. In the first stage, feature importance is evaluated by integrating spearman's rank correlation coefficient and the maximal in;
mation coefficient, which facilitates the elimination of redundant and weakly correlated features, thus reducing the search space. In the second stage, the limitations of speciation-based niching algorithms in escaping local optima are addressed by introducing a mutation mechanism based on feature importance, which is applied to the optimal particles within each niche, enabling dominated particles to escape local optima. Experimental results demonstrate that our proposed method identifies lower-dimensional, superior equivalent feature subsets across ten datasets without compromising classification accuracy.
Keywords:
Multimodal multiobjective optimization
Feature selection
Particle swarm optimization (PSO)
Niching method
Feature importance
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