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..
Distributed Collaborative Feature Selection Based on Intermediate Representation
Authors: Xiucai Ye, Hongmin Li, Akira Imakura, Tetsuya Sakurai
IJCAI 2019 | Venue PDF | 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 EMAIL, EMAIL, EMAIL, EMAIL |
| 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. |