SCAMFS: State-level Causal Asymmetry Modeling for Multi-label Feature Selection

Multi-label feature selection aims to identify compact and informative feature subsets from data with multiple correlated labels. Existing methods typically evaluate feature relevance at the label-variable level, treating each binary label as an indivisible unit. This may obscure state-specific patterns, especially when rare positive states are overwhelmed by dominant negative states, resulting in state-level dilution and label blocking. To address this issue, we propose SCAMFS, State-level Causal Asymmetry Modeling for Multi-label Feature Selection, as a causally motivated state-level feature selection framework. SCAMFS introduces a state-level decomposition strategy that decomposes each label into state-specific events, and further defines State-Specific Mutual Information (SSMI) and Differential State Mutual Information (DSMI) to model feature–state relevance and cross-label state dependencies. It also employs an explanatory competition mechanism to recover informative features masked by correlated label states. Experiments on seven real-world datasets show that SCAMFS outperforms representative baselines on most metrics. The anonymous code repository is available at: https://anonymous.4open.science/r/SCAMFS-code-0C33.