Partial Multi-Label Feature Selection via Entropy-Weighted Multi-Scale Neighborhood Granular Label Distribution Learning

Partial multi-label feature selection aims to identify discriminative features from data where each instance is associated with an ambiguous candidate label set. Existing methods are typically built upon single-scale modeling assumptions and may fail to fully exploit the multi-granularity structure underlying instance–label relationships. To address this limitation, we propose a novel framework termed PML-FSMNG, which integrates entropy-weighted multi-scale neighborhood granules with label distribution learning. Specifically, multi-scale neighborhood systems are constructed to estimate label distinguishability at multiple structural scales, and Shannon entropy is employed to adaptively fuse scale-specific label distributions into a robust soft supervisory signal. Based on the learned label distribution, an embedded sparse regression model with ℓ2,1-norm regularization is developed for discriminative feature selection, together with an entropy-regularized adaptive graph learning mechanism to preserve intrinsic geometric structure. Extensive experiments on benchmark datasets demonstrate that the proposed method consistently outperforms several state-of-the-art approaches, validating the effectiveness of multi-scale modeling and entropy-guided adaptive learning under label ambiguity.