On Imbalanced Regression with Hoeffding Trees

Pantia-Marina Alchirch; Dimitrios I. Diochnos

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arXiv · 2602.22101

On Imbalanced Regression with Hoeffding Trees

Pantia-Marina Alchirch·Dimitrios I. Diochnos

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Abstract

Many real-world applications generate continuous data streams for regression. Hoeffding trees and their variants have a long-standing tradition due to their effectiveness, either alone or as base models in broader ensembles. Recent batch-learning work shows that kernel density estimation (KDE) improves smoothed predictions in imbalanced regression [Yang et al., 2021], while hierarchical shrinkage (HS) provides post-hoc regularization for decision trees without modifying their structure [Agarwal et al., 2022]. We extend KDE to streaming settings via a telescoping formulation and integrate HS into incremental decision trees. Empirical evaluation on standard online regression benchmarks shows that KDE consistently improves early-stream performance, whereas HS provides limited gains. Our implementation is publicly available at: https://github.com/marinaAlchirch/DSFA_2026.

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Pantia-Marina Alchirch, Dimitrios I. Diochnos. 2026-02-25. On Imbalanced Regression with Hoeffding Trees. https://arxiv.org/abs/2602.22101

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