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..
Weakly Supervised Multi-Label Learning via Label Enhancement
Authors: JiaQi Lv, Ning Xu, RenYi Zheng, Xin Geng
IJCAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments across a wide range of real-world datasets clearly validate the superiority of the proposed approach. |
| Researcher Affiliation | Academia | MOE Key Laboratory of Computer Network and Information Integration, China School of Computer Science and Engineering, Southeast University, Nanjing 210096, China EMAIL |
| Pseudocode | No | The paper describes procedures and equations but does not include a formally labeled 'Pseudocode' or 'Algorithm' block. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | A total of 14 real-world LDL datasets are employed for performance evaluation 1. The binarization method [Xu et al., 2018b] is adopted to get the logical labels from the real label distributions. 1http://palm.seu.edu.cn/xgeng/LDL/index.htm#data 2http://mulan.sourceforge.net/datasets-mlc.html 3http://www.kecl.ntt.co.jp/as/members/ueda/yahoo.tar |
| Dataset Splits | Yes | Half of the instances in each dataset are randomly chosen as the training set while the other half as the test set. |
| Hardware Specification | No | The paper does not explicitly describe the hardware used to run its experiments. |
| Software Dependencies | No | The paper mentions software components and algorithms such as 'Gaussian kernel', 'k-means', and 'QP toolbox' but does not specify their version numbers for reproducibility. |
| Experiment Setup | Yes | The parameter λ in WSMLLE is chosen among {0.01, 0.1, 1} and the number of clusters g is chosen among {1, 2, ..., 10}. The kernel function is Gaussian kernel. |