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

ABSTRACT:

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 d

SUBMITTER: Bertrand F 

PROVIDER: S-EPMC8097688 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature