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[2602.12961] Ca-MCF: Category-level Multi-label Causal Feature selection
Summary
The paper introduces Ca-MCF, a novel method for category-level multi-label causal feature selection, enhancing predictive accuracy while reducing dimensionality through innovative causal modeling techniques.
Why It Matters
This research addresses limitations in existing multi-label feature selection methods by focusing on category-specific causal relationships, which can lead to more accurate models in machine learning applications. The findings are significant for practitioners seeking to improve model performance in complex datasets.
Key Takeaways
- Ca-MCF improves causal feature selection by decomposing label variables into category nodes.
- The method utilizes Specific and Distinct Category-Specific Mutual Information to enhance feature recovery.
- Extensive experiments show Ca-MCF outperforms existing benchmarks in predictive accuracy.
- Structural symmetry checks and redundancy removal ensure robustness in identified features.
- The approach is applicable across various real-world datasets, making it versatile for practitioners.
Computer Science > Machine Learning arXiv:2602.12961 (cs) [Submitted on 13 Feb 2026] Title:Ca-MCF: Category-level Multi-label Causal Feature selection Authors:Wanfu Gao, Yanan Wang, Yonghao Li View a PDF of the paper titled Ca-MCF: Category-level Multi-label Causal Feature selection, by Wanfu Gao and 2 other authors View PDF HTML (experimental) 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-...