Sequential Feature Classification in the Context of Redundancies


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.

This paper is still work in progress but is close to submission for peer review.

Experiments are reproducible and can be found here.