https://github.com/abess-team/a-splicing-approach-to-best-subset-of-groups-selection
https://github.com/abess-team/a-splicing-approach-to-best-subset-of-groups-selection
best-subset-selection group-selection high-dimensional-data
Last synced: about 2 months ago
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- Host: GitHub
- URL: https://github.com/abess-team/a-splicing-approach-to-best-subset-of-groups-selection
- Owner: abess-team
- Created: 2022-03-10T14:09:09.000Z (about 3 years ago)
- Default Branch: main
- Last Pushed: 2023-02-13T09:45:48.000Z (over 2 years ago)
- Last Synced: 2025-03-25T07:22:49.189Z (2 months ago)
- Topics: best-subset-selection, group-selection, high-dimensional-data
- Language: R
- Homepage: https://pubsonline.informs.org/doi/10.1287/ijoc.2022.1241
- Size: 4.9 MB
- Stars: 8
- Watchers: 0
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Group splicing numerical experiments
This repository contains scripts to run the synthetic datasets and real-world dataset analysis described
in *A Splicing Approach to Best Subset of Groups Selection*.
## Codes
* `Synthetic_dataset_analysis/Synthetic_dataset_analysis.R` : R script used to run the synthetic datasets analysis.
* `Real-world_dataset_analysis/Real-world_dataset_analysis.R` : R script used to run the real-world dataset analysis.
* `Real-world_dataset_analysis/trim32.rda` : Dataset used in the real-world dataset analysis.
* `gomp/gomp.R` : R interface of `gomp.cpp`.
* `gomp/gomp.cpp` : C++ implementation of group orthogonal matching pursuit (GOMP).
## Softwares
* Group Lasso : R package `grpreg` (3.4.0).
* Group MCP : R package `grpreg` (3.4.0).
* GOMP : Implementation in R language with `Rcpp` modules.
* Group Splicing : R package `abess` (0.4.0).
## Citations
Please cite the following publications if you make use of the material here.
- Yanhang Zhang, Junxian Zhu, Jin Zhu, and Xueqin Wang. A splicing approach to best
subset of groups selection. INFORMS Journal on Computing, 35(1):104–119, 2023. doi:
10.1287/ijoc.2022.1241. URL https://doi.org/10.1287/ijoc.2022.1241.
- Jin Zhu, Xueqin Wang, Liyuan Hu, Junhao Huang, Kangkang Jiang, Yanhang Zhang, Shiyun Lin and Junxian Zhu (2022). abess: A Fast Best-Subset Selection Library in Python and R. Journal of Machine Learning Research, 23(202), 1-7.
The corresponding BibteX entries:
```
@article{doi:10.1287/ijoc.2022.1241,
author = {Zhang, Yanhang and Zhu, Junxian and Zhu, Jin and Wang, Xueqin},
title = {A Splicing Approach to Best Subset of Groups Selection},
journal = {INFORMS Journal on Computing},
volume = {35},
number = {1},
pages = {104-119},
year = {2023},
doi = {10.1287/ijoc.2022.1241},
URL = {https://doi.org/10.1287/ijoc.2022.1241},
eprint = { https://doi.org/10.1287/ijoc.2022.1241}
}
```
and
```
@article{JMLR:v23:21-1060,
author = {Jin Zhu and Xueqin Wang and Liyuan Hu and Junhao Huang and Kangkang Jiang and Yanhang Zhang and Shiyun Lin and Junxian Zhu},
title = {abess: A Fast Best-Subset Selection Library in Python and R},
journal = {Journal of Machine Learning Research},
year = {2022},
volume = {23},
number = {202},
pages = {1--7},
url = {http://jmlr.org/papers/v23/21-1060.html}
}
```
## Contact
Please direct questions and comments to the [issues page](https://github.com/abess-team/Group-splicing_codes/issues).