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..
Discriminative and Correlative Partial Multi-Label Learning
Authors: Haobo Wang, Weiwei Liu, Yang Zhao, Chen Zhang, Tianlei Hu, Gang Chen
IJCAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on various real-world datasets clearly validate the superiority of our proposed method. Section 4 reports our experimental results on various real-world datasets. |
| Researcher Affiliation | Academia | Key Lab of Intelligent Computing Based Big Data of Zhejiang Province, Zhejiang University, College of Computer Science and Technology, Zhejiang University, School of Computer Science, Wuhan University |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | These datasets are collected from various real-world tasks: Image [Fang and Zhang, 2019] and Scene [Boutell et al., 2004] for image annotation, Slashdot [Read et al., 2011] for text categorization, Cal500 [Turnbull et al., 2008] and Emotions [Trohidis et al., 2008] for music classification. |
| Dataset Splits | No | The paper mentions 'All the datasets are randomly partitioned to 80% for training and the rest for testing.' but does not specify a validation split. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU, GPU models, or memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions 'Scikit-learn s [Pedregosa et al., 2011] implementation' but does not specify a version number for Scikit-learn or any other key software dependencies. |
| Experiment Setup | Yes | In this paper, δ1, δ2 are empirically set as 0.01, 1 for Cal500 and 0.01, 0.5 for other datasets. The number of boosting rounds is fixed to 10. k is set as 10 for all the nearest neighbor based algorithms. For CPLST, we take the first 5 principal components. Following the experimental setting in [Fang and Zhang, 2019], we set thr = 0.9 and α = 0.95 for PARTICLE. |