Multi-scale multi-label feature selection via fuzzy mutual information

Multi-label feature selection has gained significant attention in recent years due to the growing volume of data. Real-world phenomena often manifest intricate patterns and relationships across multiple scales or levels of granularity. Despite this complexity, existing multi-label feature selection algorithms fall short in harnessing multi-scale information effectively. Moreover, prevalent information-theoretic methods necessitate the discretization of continuous features, risking information loss in the process. In light of these challenges, a new multi-scale multi-label feature selection (MsMLFS) algorithm based on fuzzy information measures is presented. First, the multi-scale multi-label decision table and fuzzy information measures associated with it were defined. Then, the axiomatic model for multi-scale multi-label feature selection is established. Utilizing this, a multi-scale multi-label feature selection algorithm that prioritizes maximum dependency and minimal redundancy is presented. The proposed algorithm is evaluated against nine existing methods on ten benchmark multi-label datasets from various domains. The experimental findings clearly demonstrate the effectiveness of the proposed method on different evaluation metrics. Additionally, the statistical significance and stability of the proposed method is validated, further affirming its efficacy and reliability.