Graph Random Walk with Feature-Label Space Alignment: A Multi-Label Feature Selection Method
Authors: Wanfu Gao, Jun Gao, Qingqi Han, Hanlin Pan, Kunpeng Liu
IJCAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments and ablation studies conducted on seven benchmark datasets and three representative datasets using various evaluation metrics demonstrate the superiority of the proposed method. |
| Researcher Affiliation | Academia | 1College of Computer Science and Technology, Jilin University, China 2Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, China 3Department of Computer Science, Portland State University, Portland, OR 97201 USA EMAIL, EMAIL, EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Random Walk Mutual Information (RWMI) |
| Open Source Code | No | No explicit statement or link to open-source code for the methodology described in this paper was found. |
| Open Datasets | Yes | To validate the effectiveness of the proposed method under complex relationships, our experimental evaluation employs multi-label datasets from the MULAN library [Tsoumakas et al., 2011], including the web text datasets Arts, Business, Education, and Health. Additionally, the datasets encompass the Emotions dataset for music genres, the Flags dataset for image data, and the Yeast dataset for biological data. |
| Dataset Splits | Yes | Dataset #Training set #Test set #Features #Labels #Distinct #Domain Arts 2000 3000 462 26 321 139 Web text Business 2000 3000 438 30 321 139 Web text Education 2000 3000 550 33 321 139 Web text Health 2000 3000 612 32 321 139 Web text Yeast 1500 917 103 14 198 Biology Flags 129 65 19 7 54 Image Emotions 391 202 72 6 27 Music |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory amounts) used for running experiments are provided in the paper. |
| Software Dependencies | No | No specific software dependencies with version numbers (e.g., library names with versions) are explicitly mentioned in the paper. |
| Experiment Setup | Yes | The search range for the regularization parameters is set to {0.01, 0.1, 0.3, 0.5, 0.7, 0.9, 1.0}. For the random walk method, the range for nwalks is set to {100, 1000, 10000}, the range for walk length is set to {10, 20, 30}, and the ranges for jump prob and decay factor are both set to {0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9}. |