Label-Specific Multilabel Feature Selection Based on Fuzzy Implication Granularity Information

Yangding Li, Hao Xie, Jianhua Dai

Abstract: In recent years, multilabel feature selection (MFS) has gained considerable attention as a key technique in the field of data mining. Embedded methods have been widely adopted due to their simplicity and efficiency. However, most embedded MFS methods assume that all labels share the same feature space. Although previous works have noted this issue and proposed solutions, they still suffer from the inability to accurately identify a specific subset of features for each label. Furthermore, most embedded MFS methods often only consider feature similarity through manifold learning concepts, neglecting the impact of feature redundancy, leading to suboptimal performance. In addition, their weight matrix is usually derived from a single perspective of the loss function. To address these limitations, we propose a novel embedded label-specific MFS method based on fuzzy implication granularity information called LSFSFI. This method uses the partial fuzzy mutual implication granularity information to capture the relationships between labels and features, and introduces an auxiliary matrix to achieve label-specific feature selection, which provides a new perspective for the calculation of the weight matrix. In addition, the normalized fuzzy mutual implication granularity information is used to describe the redundancy between features, and a new redundancy constraint regularization term is proposed to ensure a more reasonable assignment of feature weights. Experimental results on several multilabel datasets show that LSFSFI outperforms existing methods in terms of both performance and practicality.