Sequential Feature Classification in the Context of Redundancies
This work addresses feature selection for machine learning practitioners by extending a linear method to non-linear scenarios, though it is incremental as it builds on existing approximations.
The authors tackled the problem of distinguishing between strong and weak relevance in all-relevant feature selection for non-linear cases, achieving a new solution using random forest models and statistical methods.
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.