Semi-Supervised AUC Optimization Without Guessing Labels of Unlabeled Data
Authors: Zheng Xie, Ming Li
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| 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: {xiez, lim}@lamda.nju.edu.cn |
| 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. |