AI RESEARCH
On Imbalanced Regression with Hoeffding Trees
arXiv CS.AI
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ArXi:2602.22101v3 Announce Type: replace-cross 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, 2021], while hierarchical shrinkage (HS) provides post-hoc regularization for decision trees without modifying their structure [Agarwal, 2022.