基于高阶相关性与双重冗余驱动的全局多标签特征选择

doi:10.16451/j.cnki.issn1003-6059.202601003

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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.

Key words： Fuzzy Rough Sets Feature Selection Multi-label Learning Feature Redundancy

Received: 23 December 2025

ZTFLH:

TP18

Fund:National Natural Science Foundation of China(No.12471442)

Corresponding Authors: SHE Yanhong, Ph.D., professor. His research interests include machine learning and uncertainty data modeling.

About author:: DENG Wen, Master student. His research interests include fuzzy rough sets and feature selection.
ZHENG Wenli, Ph.D., lecturer. Her research interests include machine learning and hierarchical classification.
HE Xiaoli, Ph.D., associate professor. Her research interests include uncertainty reasoning and granular computing.
QIAN Ting, Ph.D., associate professor. Her research interests include rough sets, con-cept lattices and uncertainty reasoning.

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Cite this article:

DENG Wen,SHE Yanhong,ZHENG Wenli等. Global Multi-label Feature Selection Driven by Higher-Order Correlation and Dual Redundancy[J]. Pattern Recognition and Artificial Intelligence, 2026, 39(1): 52-66.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202601003 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2026/V39/I1/52

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