Distributed Collaborative Feature Selection Based on Intermediate Representation
Authors: Xiucai Ye, Hongmin Li, Akira Imakura, Tetsuya Sakurai
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on real-world datasets demonstrate the effectiveness of the proposed method. |
| Researcher Affiliation | Academia | Xiucai Ye, Hongmin Li, Akira Imakura and Tetsuya Sakurai Department of Computer Science, University of Tsukuba yexiucai@cs.tsukuba.ac.jp, li.hongmin.xa@alumni.tsukuba.ac.jp, imakura@cs.tsukuba.ac.jp, sakurai@cs.tsukuba.ac.jp |
| Pseudocode | Yes | Algorithm 1 Distributed collaborative feature selection |
| 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 | We use four gene expression datasets for performance evaluation. The datasets are Colon, TOX-171, Lung, and Lymphoma, which are downloaded from http://featureselection.asu.edu/datasets.php. |
| Dataset Splits | Yes | For each dataset, 80% and 20% of the data are set as the training and test data in each party |
| Hardware Specification | Yes | All experiments are performed using MATLAB2018a on Mac OS X 10.13.6 (18C54) with core i7 CPU and 16GB ram. |
| Software Dependencies | Yes | All experiments are performed using MATLAB2018a on Mac OS X 10.13.6 (18C54) with core i7 CPU and 16GB ram. |
| Experiment Setup | Yes | The number of selected features is ranged from 1 to 300. α and β are tuned over {10 3, 10 2, 10 11, 101, 102, 103}. The neighborhood size is set as k = 5. Dimensionalities of embedding are set as p = 10 and q = 5. |