Sequential Feature Classification in the Context of Redundancies
Machine Learning
2020-04-17 v2 Machine Learning
Abstract
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.
Keywords
Cite
@article{arxiv.2004.00658,
title = {Sequential Feature Classification in the Context of Redundancies},
author = {Lukas Pfannschmidt and Barbara Hammer},
journal= {arXiv preprint arXiv:2004.00658},
year = {2020}
}
Comments
Added new experiment and footnote to reproducable results