Sequential Feature Classification in the Context of Redundancies

Lukas Pfannschmidt, Barbara Hammer
4/1/2020
cs.LGstat.ML

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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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Cite this paper

@article{pfannschmidt2020sequential,
  title  = {Sequential Feature Classification in the Context of Redundancies},
  author = {Lukas Pfannschmidt and Barbara Hammer},
  year   = {2020},
  eprint = {2004.00658},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/abs/2004.00658}
}

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