  One of the most common pitfalls often found in high dimensional biological
data sets are correlations between the features. This may lead to statistical
and machine learning methodologies overvaluing or undervaluing these correlated
predictors, while the truly relevant ones are ignored. In this paper, we will
define a new method called \textit{pairwise permutation algorithm} (PPA) with
the aim of mitigating the correlation bias in feature importance values.
Firstly, we provide a theoretical foundation, which builds upon previous work
on permutation importance. PPA is then applied to a toy data set, where we
demonstrate its ability to correct the correlation effect. We further test PPA
on a microbiome shotgun dataset, to show that the PPA is already able to obtain
biological relevant biomarkers.
