On Imbalanced Regression with Hoeffding Trees 事件

On Imbalanced Regression with Hoeffding Trees arXiv:2602.22101v3 Announce Type: replace-cross 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 shrin