Improving the Predictive Performance of Bootstrap Aggregating by Dirichlet Resampling

Quoc Viet Le; Joonha Park

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arXiv · 2609.21454

Improving the Predictive Performance of Bootstrap Aggregating by Dirichlet Resampling

Quoc Viet Le·Joonha Park

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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 $α>0$. 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.

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Quoc Viet Le, Joonha Park. 2026-09-18. Improving the Predictive Performance of Bootstrap Aggregating by Dirichlet Resampling. https://arxiv.org/abs/2609.21454

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