Incomplete Multi-label Feature Selection via Dynamic Dual-Graph Optimization and Fuzzy Feature Interaction

Multi-label feature selection (MFS) plays a vital role in enhancing model performance by identifying the most relevant features. However, most existing methods assume complete feature information and overlook the potential issue of missing features in real-world applications. Furthermore, existing methods mainly focus on feature redundancy while paying insufficient attention to the potential positive interaction among features, which adversely affects the effectiveness of feature selection. To overcome these limitations, this paper proposes a novel incomplete MFS method based on dynamic dual-graph optimization and fuzzy feature interaction (D2GOFI). The method addresses two core challenges systematically: feature-missing processing and feature interaction modeling. Specifically, D2GOFI introduces a fuzzy tolerance relation to mitigate information loss caused by missing features, and proposes fuzzy tolerance implication granularity information to reveal the data's intrinsic structure and guide the feature selection process effectively. To address the potential issue of over-inclusivity introduced by the fuzzy tolerance relation, a non-negative adjustment matrix has been designed to improve the stability and reliability of feature selection. Meanwhile, D2GOFI explicitly models positive fuzzy interaction between features to enhance the expressiveness of feature relevance evaluation. Finally, a dynamic dual-graph optimization strategy is employed to preserve local manifold structures at the feature and label levels simultaneously, ensuring the feature selection process aligns with the data's inherent patterns. Experimental results demonstrate that D2GOFI achieves superior accuracy and robustness across multiple datasets.