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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
Semi-Supervised AUC Optimization Without Guessing Labels of Unlabeled Data
Authors: Zheng Xie, Ming Li
AAAI 2018 | Venue PDF | LLM Run Details | Input Tokens: 17,017 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,987 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results indicate that the proposed methods outperform the existing methods. |
| Researcher Affiliation | Academia | Zheng Xie, Ming Li National Key Laboratory for Novel Software Technology, Nanjing University Collaborative Innovation Center for Novel Software Technology and Industrialization Nanjing 210023, China Email: EMAIL |
| Pseudocode | Yes | Algorithm 1 SAMULT |
| Open Source Code | No | No explicit statement regarding open-source code availability or a direct link is provided. |
| Open Datasets | Yes | We evaluate our methods on 20 widely-used datasets, including 18 datasets from UCI repository (Lichman 2013), and the ijcnn1 and the madelon (Guyon et al. 2005). |
| Dataset Splits | Yes | The parameters are chosen by grid search through a 5-fold cross validation. For each experiment, 10% of the data in the training set is labeled for SAMULT and SAMULTP+U to train the classifiers, and SAMULTP+U uses only positive and unlabeled data. Roughly 20% data is held out as the test set. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory) are provided for the experimental setup. |
| Software Dependencies | No | No specific software dependencies with version numbers are provided in the paper. |
| Experiment Setup | Yes | The parameters are chosen by grid search through a 5-fold cross validation. The number of base learners in SAMPURA is fixed on 20. |