Abstract：Multi-label feature selection is a critical preprocessing technique for handling high-dimensional multi-label data. However, existing approaches are often trapped in local optima due to greedy search strategies or unadequate measuring feature correlation and redundancy within sparse models. To address these issues, a global multi-label feature selection algorithm driven by higher-order correlation and dual redundancy(GHC-DR) is proposed. First, a fuzzy dependency measure based on multi-label k-nearest neighbors is introduced to accurately evaluate the higher-order correlations between features and the label system. Second, GHC-DR is designed to focus on the local geometric structure of features by constructing a feature graph to capture local similarities among features, and a dual redundancy evaluation mechanism fusing information theory with local structure is developed. Finally, higher-order correlation, dual redundancy and label correlations are integrated into a unified sparse learning objective function, and an efficient closed-form solution is derived. Experiments on 15 public multi-label benchmark datasets demonstrate the superior performance of GHC-DR across multiple evaluation metrics.
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2026, Vol. 39 
