AcSel: selecting variables with accuracy in correlated datasets

TL;DR

AcSel is a wrapping algorithm designed to improve the accuracy of variable selection methods, especially in datasets with highly correlated variables, by leveraging intensive computational simulations.

Contribution

The paper introduces AcSel, a novel wrapping algorithm that enhances variable selection accuracy in correlated datasets, addressing limitations of existing methods like Lasso.

Findings

AcSel improves variable selection accuracy in correlated datasets.

AcSel outperforms traditional methods in simulation studies.

The approach is versatile and can enhance various existing selection techniques.

Abstract

With the emergence of high-throughput technologies, it is possible to measure large amounts of data relatively at low cost. Such situations arise in many fields from sciences to humanities, and variable selection may be of great help to answer challenges that are specific to each of them. Variable selection may allow to know, among all measured variables, which are of interest and which are not. A lot of methods have been proposed to handle this issue, with the Lasso and other penalized regression as special cases. These methods fail in some cases and linear correlation between explanatory variables is the most common of these, especially in big datasets. In this article, we introduce AcSel, a wrapping algorithm able to enhance the accuracy of any variable selection method. To achieve this result, we use intensive computational simulations.