SelectBoost: a general algorithm to enhance the performance of variable selection methods

Frédéric Bertrand, Ismaïl Aouadi, Nicolas Jung, Raphael Carapito, Laurent Vallat, Seiamak Bahram, Myriam Maumy-Bertrand, Lenore Cowen
2020 Bioinformatics  
Motivation With the growth of big data, variable selection has become one of the critical challenges in statistics. Although many methods have been proposed in the literature their performance in terms of recall (sensitivity) and precision (PPV) is limited in a context where the number of variables by far exceeds the number of observations or in a highly correlated setting. Results In this article, we propose a general algorithm which improves the precision of any existing variable selection
more » ... hod. This algorithm is based on highly intensive simulations and takes into account the correlation structure of the data. Our algorithm can either produce a confidence index for variable selection or be used in an experimental design planning perspective. We demonstrate the performance of our algorithm on both simulated and real data. We then apply it in two different ways to improve biological network reverse-engineering. Availability Code is available as the SelectBoost package on the CRAN, https://cran.r-project.org/package=SelectBoost. Some network reverse-engineering functionalities are available in the Patterns CRAN package, https://cran.r-project.org/package=Patterns. Supplementary information Supplementary data are available at Bioinformatics online.
doi:10.1093/bioinformatics/btaa855 pmid:33016991 pmcid:PMC8097688 fatcat:u6efdyuptfemtcfxrz2yqfo2f4