Sequential Feature Classification in the Context of Redundancies [article]

Lukas Pfannschmidt, Barbara Hammer
2020 arXiv   pre-print
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.
arXiv:2004.00658v2 fatcat:2k36qo62ijcg5nk3rs2iuaqurm