Robust Feature Selection on Incomplete Data
Authors: Wei Zheng, Xiaofeng Zhu, Yonghua Zhu, Shichao Zhang
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| 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. |