  The problem of all-relevant feature selection is concerned with finding a
relevant feature set with preserved redundancies. There exist several
approximations to solve this problem but only one could give a distinction
between strong and weak relevance. This approach was limited to the case of
linear problems. In this work, we present a new solution for this distinction
in the non-linear case through the use of random forest models and statistical
methods.
