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..
Solving the Partial Label Learning Problem: An Instance-Based Approach
Authors: Min-Ling Zhang, Fei Yu
IJCAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments show that IPAL compares favorably against the existing instance-based as well as other stateof-the-art partial label learning approaches.In this paper, two series of comparative experiments are conducted on controlled UCI data sets [Bache and Lichman, 2013] as well as real-world partial label data sets. |
| Researcher Affiliation | Academia | 1School of Computer Science and Engineering, Southeast University, Nanjing 210096, China 2Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education, China |
| Pseudocode | Yes | Table 1: The pseudo-code of IPAL. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described, nor does it explicitly state its release. |
| Open Datasets | Yes | two series of comparative experiments are conducted on controlled UCI data sets [Bache and Lichman, 2013] as well as real-world partial label data sets.Table 2 summarizes characteristics of these experimental data sets. |
| Dataset Splits | Yes | ten-fold cross-validation is performed on each artificial as well as real-world partial label data set. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., 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 | As shown in Table 1, parameters employed by IPAL are set as k = 10, α = 0.95 and T = 100. |