Sequential Feature Classification in the Context of Redundancies

Lukas Pfannschmidt; Barbara Hammer

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arXiv · 2004.00658

Sequential Feature Classification in the Context of Redundancies

Lukas Pfannschmidt·Barbara Hammer

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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.

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Lukas Pfannschmidt, Barbara Hammer. 2020-04-01. Sequential Feature Classification in the Context of Redundancies. https://arxiv.org/abs/2004.00658

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