Improving the Predictive Performance of Bootstrap Aggregating by Dirichlet Resampling

Abstract:We revisit Breiman's observation that reducing inter-tree correlation without weakening individual trees can improve random forests. Building on this principle, we introduce two variants: Dirichlet-Multinomial Bagging Random Forest (DM) and Dirichlet-Weighted Random Forest (DW). Both modulate sample reweighting via a concentration parameter $\alpha>0$. We provide a simple theoretical criterion that clarifies when these variants behave indistinguishably from standard random forests, and we use it to guide a lightweight tuning strategy. In a controlled evaluation on public classification benchmarks, DM and DW are consistently competitive and often stronger than other random-forest (RF) baselines, with negligible additional runtime.

29 pages (10 main text, 19 pages appendix), 21 tables, 3 algorithms. No figures

Machine Learning (stat.ML); Machine Learning (cs.LG); Methodology (stat.ME)

arXiv:2609.21454 [stat.ML]

(or arXiv:2609.21454v1 [stat.ML] for this version)

https://doi.org/10.48550/arXiv.2609.21454 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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