Sequential Feature Classification in the Context of Redundancies

Code

• github.com/lpfann/squamish
  OfficialIn papernone★ 0
• github.com/lpfann/squamish_experiments
  OfficialIn papernone★ 0
• github.com/FabianHinder/drifting-features-analysis
  none★ 5

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.

Tasks

Reproductions