Evolutionary Transfer Optimization Assisted by Unselected Features for Multiobjective Feature Selection

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SPTMFS significantly enhances classification accuracy by incorporating unselected features, addressing a critical gap in feature selection.
In experiments across 16 datasets, SPTMFS outperformed standard multiobjective feature selection methods, confirming its effectiveness.
This method employs a novel evolutionary transfer optimization framework, integrating auxiliary tasks to streamline feature selection.
Robust statistical analyses validate SPTMFS's performance, indicating its competitive advantage over traditional methods.

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The SPTMFS method improves classification accuracy in multiobjective feature selection, suggesting enhanced optimization through unselected features.

Liu et al. (2025) studied this question.

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The SPTMFS method improves classification accuracy in multiobjective feature selection, suggesting enhanced optimization through unselected features.