  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.
