Partial Multilabel Learning via Dynamic Fuzzy Aggregations of Multigranularity Features

Partial multilabel learning is a pivotal area in machine learning that tackles scenarios where training instances are annotated with a set of candidate labels, only a subset of which is relevant. Existing approaches typically rely on global-level feature learning or noise disambiguation; however, they often struggle to effectively capture the multigranularity relationships inherent in feature and label spaces, and tend to overlook critical intrafeature information essential for accurate label discrimination. To address these limitations, we propose a novel partial multilabel learning framework based on a dynamic coarse-to-fine granularity feature aggregation strategy, which hierarchically extracts feature representations across multiple levels of granularity and dynamically emphasizes label-relevant feature components. Specifically, the dynamic fine-granularity graph captures label-specific local information by modeling the fuzzy aggregations among fine-granularity feature components, while the dynamic coarse-granularity graph learns adaptive label representations by identifying feature-aware correlations of labels and suppressing noise. By jointly leveraging these two complementary granularity levels, the model effectively integrates multilevel semantic relationships and enhances the overall discriminative capacity of the learned features. Extensive experiments conducted on benchmark datasets under varying noise conditions demonstrate that the proposed method consistently outperforms state-of-the-art approaches in partial multilabel classification.