selectBoost : a general algorithm to enhance the performance of variable selection methods - CNRS - Centre national de la recherche scientifique Accéder directement au contenu
Article Dans Une Revue Bioinformatics Année : 2021

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

Résumé

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 (predictive positive value) 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 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 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.
Fichier principal
Vignette du fichier
islandora_124934.pdf (859.58 Ko) Télécharger le fichier
Origine : Fichiers éditeurs autorisés sur une archive ouverte

Dates et versions

hal-03206128 , version 1 (28-10-2021)

Identifiants

Citer

Frédéric Bertrand, Ismaïl Aouadi, Nicolas Jung, Raphael Carapito, Laurent Vallat, et al.. selectBoost : a general algorithm to enhance the performance of variable selection methods. Bioinformatics, 2021, 37 (5), pp.659-668. ⟨10.1093/bioinformatics/btaa855⟩. ⟨hal-03206128⟩
319 Consultations
49 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More