Random boosting and random^2 forests -- A random tree depth injection approach

• URL: http://arxiv.org/abs/2009.06078v1
• Date: Sun, 13 Sep 2020 20:14:50 GMT
• Title: Random boosting and random^2 forests -- A random tree depth injection approach
• Authors: Tobias Markus Krabel, Thi Ngoc Tien Tran, Andreas Groll, Daniel Horn, Carsten Jentsch
• Abstract summary: We propose and examine a novel random tree depth injection approach suitable for sequential and parallel tree-based approaches including Boosting and Random Forests. The resulting methods are called emphRandom Boost and emphRandom$2$ Forest.
• Score: 0.1749935196721634
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The induction of additional randomness in parallel and sequential ensemble methods has proven to be worthwhile in many aspects. In this manuscript, we propose and examine a novel random tree depth injection approach suitable for sequential and parallel tree-based approaches including Boosting and Random Forests. The resulting methods are called \emph{Random Boost} and \emph{Random$^2$ Forest}. Both approaches serve as valuable extensions to the existing literature on the gradient boosting framework and random forests. A Monte Carlo simulation, in which tree-shaped data sets with different numbers of final partitions are built, suggests that there are several scenarios where \emph{Random Boost} and \emph{Random$^2$ Forest} can improve the prediction performance of conventional hierarchical boosting and random forest approaches. The new algorithms appear to be especially successful in cases where there are merely a few high-order interactions in the generated data. In addition, our simulations suggest that our random tree depth injection approach can improve computation time by up to 40%, while at the same time the performance losses in terms of prediction accuracy turn out to be minor or even negligible in most cases.

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