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Multi-phase evolutionary search space shrinking for large-scale multi-objective feature selection
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DOI：10.1016/j.asoc.2025.113755.png)
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Abstract
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• LMSSS shrinks the search space using correlation, frequency, and NDS-guided ranking. • Voting-based crossover resolves parent disagreement by accuracy-weighted choices. • Early-stage mutation revives features absent in the population to prevent lock-in. • Two-phase evaluation cuts runtime by working on smaller set of features, not full dimension. • On 15 datasets, LMSSS improves HV/IGD and MCE with significant gains over SOTA algorithms.
Keywords:
Feature selection
Large-scale
Multi-objective optimization
Search space shrinking
Crossover
Mutation
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