Dynamic Random Forests

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.

► A new principle of Random Forest (RF) induction called Dynamic Random Forests (DRF) is presented.
► A first variant of this learning principle is proposed and evaluated.
► DRF outperforms the reference RF algorithms.