MCF-Spouse: A Multi-Label Causal Feature Selection Method with Optimal Spouses Discovery
Ma, Lin Hu, Liang Huang, Qiang Hao, Pingting Hu, Juncheng
Ma, Lin
Hu, Liang
Huang, Qiang
Hao, Pingting
Hu, Juncheng
Supervisor
Department
Machine Learning
Embargo End Date
Type
Conference proceeding
Date
2025
License
Language
English
Collections
Research Projects
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Abstract
Multi-label causal feature selection has garnered considerable attention for its ability to identify the most informative features while accounting for the causal dependencies between labels and features. However, previous work often overlooks the unique contributions of labels to the target variables in multi-label settings, focusing instead on prioritizing feature variables. Moreover, existing methods typically rely on traditional Markov Blanket (MB) discovery to construct an initial MB, which often fails to explore the most valuable form of spouse variables to feature selection in multi-label scenarios, leading to significant computational overhead due to redundant Conditional Independence (CI) tests required for spouse search. To address these challenges, we propose the Multi-label Causal Feature Selection Method with Optimal Spouses Discovery, MCF-Spouse, which leverages mutual information to quantify the contributions of both labels and features, ensuring the retention of the most informative variables in multi-label settings. Moreover, we systematically analyzes all potential forms of spouse variables to identify the optimal spouse case, significantly reducing the spouse search space and alleviating the time overhead associated with CI tests. Experiments conducted on diverse real-world datasets demonstrate that MCF-Spouse consistently outperforms state-of-the-art methods across multiple metrics, offering a scalable and interpretable solution for multi-label causal feature selection.
Citation
L. Ma, L. Hu, Q. Huang, P. Hao, and J. Hu, “MCF-Spouse: A Multi-Label Causal Feature Selection Method with Optimal Spouses Discovery,” Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, vol. 1, pp. 5914–5922, Sep. 2025, doi: 10.24963/IJCAI.2025/658
Source
Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence
Conference
34 International Joint Conference on Artificial Intelligence (IJCAI-25)
Keywords
Causality, Bayesian learning, Multi-label learning
Subjects
Source
34 International Joint Conference on Artificial Intelligence (IJCAI-25)
Publisher
International Joint Conferences on Artificial Intelligence