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• W3085805501 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." @default.
• W3085805501 created "2020-09-21" @default.
• W3085805501 creator A5013581565 @default.
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• W3085805501 date "2020-09-13" @default.
• W3085805501 modified "2023-09-27" @default.
• W3085805501 title "Random boosting and random^2 forests -- A random tree depth injection approach" @default.
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• W3085805501 hasPublicationYear "2020" @default.
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