Solving the Partial Label Learning Problem: An Instance-Based Approach
Authors: Min-Ling Zhang, Fei Yu
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | 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. |