Ca-MCF: Category-level Multi-label Causal Feature selection

• URL: http://arxiv.org/abs/2602.12961v1
• Date: Fri, 13 Feb 2026 14:26:47 GMT
• Title: Ca-MCF: Category-level Multi-label Causal Feature selection
• Authors: Wanfu Gao, Yanan Wang, Yonghao Li,
• Abstract summary: We propose a Category-level Multi-label Causal Feature selection method named Ca-MCF.<n>Ca-MCF utilizes label category flattening to decompose label variables into specific category nodes, enabling precise modeling of causal structures within the label space.
• Score: 13.546912368053661
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-label causal feature selection has attracted extensive attention in recent years. However, current methods primarily operate at the label level, treating each label variable as a monolithic entity and overlooking the fine-grained causal mechanisms unique to individual categories. To address this, we propose a Category-level Multi-label Causal Feature selection method named Ca-MCF. Ca-MCF utilizes label category flattening to decompose label variables into specific category nodes, enabling precise modeling of causal structures within the label space. Furthermore, we introduce an explanatory competition-based category-aware recovery mechanism that leverages the proposed Specific Category-Specific Mutual Information (SCSMI) and Distinct Category-Specific Mutual Information (DCSMI) to salvage causal features obscured by label correlations. The method also incorporates structural symmetry checks and cross-dimensional redundancy removal to ensure the robustness and compactness of the identified Markov Blankets. Extensive experiments across seven real-world datasets demonstrate that Ca-MCF significantly outperforms state-of-the-art benchmarks, achieving superior predictive accuracy with reduced feature dimensionality.

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