Progressive Label Propagation for Semi-Supervised Multi-Dimensional Classification
Authors: Teng Huang, Bin-Bin Jia, Min-Ling Zhang
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments validate the effectiveness of the proposed approach.To evaluate PLAP s effectiveness, we construct SSMDC testbed with ten real-world MDC data sets and compare PLAP with its degenerated version which deals with each class space independently via label propagation as well as six state-of-the-art MDC approaches which only use labeled MDC samples to induce supervised models. Experimental results clearly show that PLAP achieves superior performance against both its degenerated version and existing MDC approaches. |
| Researcher Affiliation | Academia | Teng Huang1,3 , Bin-Bin Jia2 , Min-Ling Zhang1,3 1School of Computer Science and Engineering, Southeast University, Nanjing 210096, China 2College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China 3Key Lab. of Computer Network and Information Integration (Southeast University), MOE, China {tengh, zhangml}@seu.edu.cn, jiabinbin@lut.edu.cn |
| Pseudocode | No | The paper describes the PLAP approach using mathematical equations and textual explanations, but does not include 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 | In this paper, we use ten real-world MDC data sets for experimental studies where Table 1 summarizes their basic characteristics...(Table 1 lists: Edm, Song, WQpla., WQani., WQ, Be La E, Thyroid, Pain, Disfa, Adult). |
| Dataset Splits | No | For each data set, we randomly sample 40, 50 and 60 examples to form the labeled data set Dl while the remaining examples are used to form the unlabeled data set Du. The sampling procedure is repeated ten times, and the mean metric values as well as standard deviations are recorded. (No specific mention of a separate validation set for model tuning.) |
| Hardware Specification | No | We thank the Big Data Center of Southeast University for providing the facility support on the numerical calculations in this paper. (No specific hardware models like CPU, GPU, or memory details are provided.) |
| Software Dependencies | No | The paper describes hyperparameter settings but does not list specific software dependencies with version numbers (e.g., programming languages, libraries, or frameworks). |
| Experiment Setup | Yes | For PLAP and PLAPd, the bandwidth parameter in Eq.(1) is fixed as 50, the trade-off parameter α in Eq.(2) is fixed as 0.99, and the number of nearest neighbors k in Eq.(6) is fixed as 7. For each data set, we randomly sample 40, 50 and 60 examples to form the labeled data set Dl while the remaining examples are used to form the unlabeled data set Du. The sampling procedure is repeated ten times, and the mean metric values as well as standard deviations are recorded. |