Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1]
MCF-Spouse: A Multi-Label Causal Feature Selection Method with Optimal Spouses Discovery
Authors: Lin Ma, Liang Hu, Qiang Huang, Pingting Hao, Juncheng Hu
IJCAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments conducted on diverse real-world datasets demonstrate that MCF-Spouse consistently outperforms stateof-the-art methods across multiple metrics, offering a scalable and interpretable solution for multi-label causal feature selection. The code is available at https://github.com/malinjlu/MCF-Spouse. |
| Researcher Affiliation | Academia | 1College of Computer Science and Technology, Jilin University 2Department of Machine Learning, Mohamed Bin Zayed University of Artificial Intelligence 3College of Computer Science and Information Technology, Northeast Normal University |
| Pseudocode | Yes | Algorithm 1 MCF-Spouse Algorithm |
| Open Source Code | Yes | The code is available at https://github.com/malinjlu/MCF-Spouse. |
| Open Datasets | Yes | 1) Datasets: we utilize eight real-world datasets sourced from various application domains, including Flags [Gonc alves et al., 2013], Virus GO [Xu et al., 2016], CHD 49 [Shao et al., 2013], Plant GO [Xu et al., 2016], Enron [Read et al., 2008], Image [Zhang and Zhou, 2007], Yeast [Elisseeff and Weston, 2001], and Human GO [Xu et et al., 2016] detailed in Table 1. These datasets are accessible for download from the Multi Label Classification Dataset Repository1. 1http://www.uco.es/kdis/mllresources/#3sourcesDesc |
| Dataset Splits | No | The paper does not explicitly provide specific training/test/validation dataset splits (e.g., percentages, sample counts, or explicit cross-validation folds for the datasets) within the main text. It mentions parameters for feature selection and model evaluation but not how the datasets themselves were partitioned. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions using "ML-k NN [Zhang and Zhou, 2007], with the number of nearest neighbors k fixed at 10" for classification, but it does not specify any software versions for libraries, frameworks, or programming languages used in the implementation. |
| Experiment Setup | Yes | Classification accuracy is computed using the ML-k NN [Zhang and Zhou, 2007], with the number of nearest neighbors k fixed at 10. Each experiment is repeated 10 times. For D2F, MCMFS, ENM, and FSSL, these mutual information-based methods can not determine the optimal number of features. Therefore, we gradually increase the percentage of selected features from 1% to 20% with a step size of 1%. For SFUS, the value of α and β are searched within the range of {10 3, 10 2,..., 102, 103}. Similarly, for MDFS, the values of α, β, and γ are searched within the same range. In the case of ESRFS, the values of α, β, γ, and λ are also evaluated within {10 3, 10 2,..., 102, 103}. For MB-MCF, M2LC, and MCF-Spouse, these BN-based methods do not require the number of selected features to be predetermined. However, MB-MCF and M2LC have parameters: k1 which determines the number of ignored features to be recovered, and k2, which selects the features that need to undergo symmetry checks. We perform the grid search within the range of [0.1, 1] to find the values of k1 and k2 that yield the best results. For parameter k1 in MCF-Spouse, we also perform the grid search within the range of [0.1, 1] to find the optimal performance. |