Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Robust Feature Selection on Incomplete Data
Authors: Wei Zheng, Xiaofeng Zhu, Yonghua Zhu, Shichao Zhang
IJCAI 2018 | Venue PDF | LLM Run Details | Input Tokens: 14,987 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,459 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on both real and artificial incomplete data sets demonstrated that our proposed method outperformed the feature selection methods under comparison in terms of clustering performance. |
| Researcher Affiliation | Academia | Guangxi Key Lab of Multi-source Information Mining & Security, Guangxi Normal University, Guilin, 541004, China 2 Guangxi University, Nanning, 530004, China |
| Pseudocode | No | The paper describes mathematical formulations and an optimization strategy, but it does not include a distinct pseudocode block or algorithm listing. |
| Open Source Code | No | The paper does not provide a statement or link indicating that the source code for their methodology is openly available. |
| Open Datasets | Yes | We downloaded four real incomplete data sets from UCI website, i.e., Advertisement, Arrhythmia, Cvpu, and Mice |
| Dataset Splits | Yes | We employed the 10-fold cross-validation scheme to repeat every method on a data set ten times. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for software dependencies or libraries used in the experiments. |
| Experiment Setup | Yes | We set the ranges of the parameters of the comparison methods according to the corresponding literature, and set the ranges of the parameter (i.e., λ) of our method in Eq. (8) as {10 3, 10 2, ..., 103}. We further set the number of clusters in k-means clustering as the number of real classes of the data sets. |