Dynamic Random Forests

Simon Bernard (1) , Sébastien Adam (1) , Laurent Heutte (1)

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In this paper, we introduce a new Random Forest (RF) induction algorithm called Dynamic Random Forest (DRF) which is based on an adaptative tree induction procedure. The main idea is to guide the tree induction so that each tree will complement as much as possible the existing trees in the ensemble. This is done here through a resampling of the training data, inspired by boosting algorithms, and combined with other randomization processes used in traditional RF methods. The DRF algorithm shows a significant improvement in terms of accuracy compared to the standard static RF induction algorithm.

Mots clés: Random forests; Ensemble of classifiers; Random feature selection; Dynamic induction

Domaines: Traitement du texte et du document

Simon Bernard, Sébastien Adam, Laurent Heutte. Dynamic Random Forests. Pattern Recognition Letters, 2012, 33 (12), pp.1580-1586. ⟨10.1016/j.patrec.2012.04.003⟩. ⟨hal-00710083⟩