Random boosting and random^2 forests -- A random tree depth injection approach
This work addresses efficiency and accuracy challenges in machine learning for practitioners using boosting and random forests, but it is incremental as it extends existing methods with a novel injection technique.
The authors tackled the problem of improving prediction performance and computation time in tree-based ensemble methods by proposing a random tree depth injection approach, resulting in Random Boost and Random^2 Forest, which can improve computation time by up to 40% with minor or negligible performance losses in most cases.
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.