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 and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Double-Layer Hybrid-Label Identification Feature Selection for Multi-View Multi-Label Learning
Authors: Pingting Hao, Kunpeng Liu, Wanfu Gao
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on six datasets demonstrate the effectiveness and superiority of our method compared with the state-of-the-art methods. Experiments Experimental Setup Datasets. Following (Zhu, Li, and Zhang 2015; Zhang et al. 2020b; Li and Chen 2021), six popular multiview multi-label datasets are adopted to facilitate a fair result comparison with state-of-the-art methods, including SCENE, OBJECT, MIRFlickr, Corel5K, IAPRTC12 and 3Sources. |
| Researcher Affiliation | Academia | 1 College of Computer Science and Technology, Jilin University, China 2 Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, China 3 Department of Computer Science, Portland State University, Portland, OR 97201 USA |
| Pseudocode | Yes | Algorithm 1: Double-layer Hybrid-Label Identification |
| Open Source Code | No | The paper does not include an unambiguous statement about releasing code or a direct link to a source-code repository for the described methodology. |
| Open Datasets | Yes | Datasets. Following (Zhu, Li, and Zhang 2015; Zhang et al. 2020b; Li and Chen 2021), six popular multiview multi-label datasets are adopted to facilitate a fair result comparison with state-of-the-art methods, including SCENE, OBJECT, MIRFlickr, Corel5K, IAPRTC12 and 3Sources. |
| Dataset Splits | Yes | On each dataset, five-fold cross-validation is performed and the mean accuracy, as well as standard deviation, are reported. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | The parameters of the regularization paradigm are searched in set {10 3, 10 2, ..., 103}, and four metrics commonly used in this field are selected to evaluate these methods. |