selectBoost: a general algorithm to enhance the performance of variable selection methods in correlated datasets
Abstract
Motivation: With the growth of big data, variable selection has become one of the major challenges in statistics. Although many methods have been proposed in the literature their performance in terms of recall and precision are limited in a context where the number of variables by far exceeds the number of observations or in a high correlated setting. Results: In this article, we propose a general algorithm which improves the precision of any existing variable selection method. 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 it can be used in an experimental design planning perspective. We demonstrate the performance of our algorithm on both simulated and real data.
Cite
@article{arxiv.1810.01670,
title = {selectBoost: a general algorithm to enhance the performance of variable selection methods in correlated datasets},
author = {Ismaïl Aouadi and Nicolas Jung and Raphael Carapito and Laurent Vallat and Seiamak Bahram and Myriam Maumy-Bertrand and Frédéric Bertrand},
journal= {arXiv preprint arXiv:1810.01670},
year = {2021}
}
Comments
This article supersedes arXiv:1512.03307