Multi-label feature selection via entropy-aware correlation metrics and adaptive operator-guided grey wolf optimization
摘要
High-dimensional multi-label datasets often exhibit complex and nonlinear dependencies among features and labels, posing significant challenges for effective feature selection (FS). While filter-based approaches offer scalability, they often overlook label structure, whereas traditional wrapper methods incur high computational costs due to repeated classifier training. To address these limitations, we propose a novel final-model-independent wrapper-based FS method, grey wolf optimizer with adaptive operator selection for multi-label feature selection (GWO-AOSMFS). The method employs a binary grey wolf optimizer enhanced with a reinforcement-based adaptive mutation strategy to effectively explore the discrete feature space. A multi-criteria fitness function is formulated to jointly model linear feature–label correlations, nonlinear relevance via entropy-weighted mutual information, and label co-occurrence preservation through cosine similarity using a lightweight kNN projection mechanism. Unlike traditional wrapper methods that require repeated training of complex classifiers, GWO-AOSMFS evaluates feature subsets using a composite fitness function that avoids training any final classifier. A lightweight, training-free kNN module is used only to project labels for co-occurrence preservation, not for predictive modeling. To promote compactness and interpretability, a sparsity-inducing penalty is included. This design enables robust evaluation of feature subsets without the computational burden of iterative classifier invocation. Extensive experiments on thirteen benchmark datasets demonstrate that GWO-AOSMFS achieves the highest Acc 0.5155,