preprint · arXiv (Cornell University) · 2020

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

Tobias Markus Krabel, Thi Ngoc Tien Tran, Andreas Groll, Daniel Horn, Carsten Jentsch

Cited by 0Open access

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.

Cite this paper

APA 7

Krabel, T. M., Tran, T. N. T., Groll, A., Horn, D., & Jentsch, C. (2020). Random boosting and random^2 forests -- A random tree depth injection approach. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2009.06078

MLA 9

Krabel, Tobias Markus, et al. “Random boosting and random^2 forests -- A random tree depth injection approach.” arXiv (Cornell University), 2020, https://doi.org/10.48550/arxiv.2009.06078.

Chicago

Krabel, Tobias Markus, Thi Ngoc Tien Tran, Andreas Groll, Daniel Horn, and Carsten Jentsch. 2020. “Random boosting and random^2 forests -- A random tree depth injection approach.” arXiv (Cornell University). https://doi.org/10.48550/arxiv.2009.06078.

Harvard

Krabel, T.M. et al. (2020) ‘Random boosting and random^2 forests -- A random tree depth injection approach’, arXiv (Cornell University). Available at: https://doi.org/10.48550/arxiv.2009.06078.

BibTeX

@misc{krabel2020random,
  title = {{Random boosting and random^2 forests -- A random tree depth injection approach}},
  author = {Krabel, Tobias Markus and Tran, Thi Ngoc Tien and Groll, Andreas and Horn, Daniel and Jentsch, Carsten},
  journal = {arXiv (Cornell University)},
  year = {2020},
  doi = {10.48550/arxiv.2009.06078}
}

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