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An Improved Evolutionary Algorithm with Joint Encoding for Multiobjective Feature Selection in Classification
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DOI：10.1109/tevc.2026.3744732.png)
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Abstract
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Feature selection is a complex multiobjective optimization problem that requires balancing the number of selected features and classification performance. Redundant features and complex feature interactions pose significant challenges for traditional multiobjective feature selection algorithms. Existing methods typically employ either discrete or continuous encoding schemes, both of which have inherent limitations, i.e., discrete encoding struggles to capture complex feature interactions, while continuous encoding incurs high computational costs when handling redundant features. To address these issues, this paper proposes a new evolutionary multiobjective feature selection algorithm with a joint encoding mechanism, incorporating a novel joint encoding region partitioning method, an effective initialization strategy, and a new search strategy. Specifically, the joint encoding region partitioning method groups features into discrete or continuous search regions based on their importance and label relevance. An effective initialization strategy is designed to generate high-quality initial solutions in both regions. In addition, a new search strategy dynamically adjusts the number of features in discrete search regions to improve exploration. Experimental results on 16 datasets show that the proposed approach surpasses nine evolutionary multiobjective feature selection algorithms. The proposed approach effectively enhances population diversity while achieving a better balance between the number of selected features and classification performance.
Keywords:
Feature Selection
Multiobjective Optimization
Evolutionary Computation
Classification
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