A novel feature selection algorithm for linear regression called BOSO (Bilevel Optimization Selector Operator). The main contribution is the use a bilevel optimization problem to select the variables in the training problem that minimize the error in the validation set. Preprint available: [Valcarcel, L. V., San Jose-Eneriz, E., Cendoya, X., Rubio, A., Agirre, X., Prosper, F., & Planes, F. J. (2020). "BOSO: a novel feature selection algorithm for linear regression with high-dimensional data." bioRxiv. <doi:10.1101/2020.11.18.388579>]. In order to run the vignette, it is recommended to install the 'bestsubset' package, using the following command: devtools::install_github(repo="ryantibs/best-subset", subdir="bestsubset"). If you do not have gurobi, run devtools::install_github(repo="lvalcarcel/best-subset", subdir="bestsubset").
Package details |
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Author | Luis V. Valcarcel [aut, cre, ctb] (<https://orcid.org/0000-0003-3769-5419>), Edurne San Jose-Eneriz [aut] (<https://orcid.org/0000-0001-5786-5273>), Xabier Cendoya [aut, ctb] (<https://orcid.org/0000-0001-8401-4087>), Angel Rubio [aut, ctb] (<https://orcid.org/0000-0002-3274-2450>), Xabier Agirre [aut] (<https://orcid.org/0000-0002-6558-9560>), Felipe Prósper [aut] (<https://orcid.org/0000-0001-6115-8790>), Francisco J. Planes [aut, ctb] (<https://orcid.org/0000-0003-1155-3105>) |
Maintainer | Luis V. Valcarcel <lvalcarcel@unav.es> |
License | GPL-3 |
Version | 1.0.3 |
Package repository | View on CRAN |
Installation |
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