BOSO: Bilevel Optimization Selector Operator

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

AuthorLuis V. Valcarcel [aut, cre, ctb] (<>), Edurne San Jose-Eneriz [aut] (<>), Xabier Cendoya [aut, ctb] (<>), Angel Rubio [aut, ctb] (<>), Xabier Agirre [aut] (<>), Felipe Prósper [aut] (<>), Francisco J. Planes [aut, ctb] (<>)
MaintainerLuis V. Valcarcel <>
Package repositoryView on CRAN
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BOSO documentation built on July 1, 2021, 9:08 a.m.