Hoeffding adaptive splitting trees for data stream classification with concept drift and ensemble learning

Excerpt

arXiv:2608.16659v1 Announce Type: cross Abstract: Ensembles of decision trees are well-established methods for data stream classification. In ensemble learning, Hoeffding Trees are widely adopted as base learners, performing periodic split attempts according to the Hoeffding bound. Recent studies, however, indicate that this standard splitting mechanism lacks adaptability, while adaptive trees that trigger splits in response to performance degradation have achieved superior results. In this pape