An Eager Splitting Strategy for Online Decision Trees

no code implementations • 20 Oct 2020 • Chaitanya Manapragada, Heitor M Gomes, Mahsa Salehi, Albert Bifet, Geoffrey I Webb

In this work, we study in ensemble settings the effectiveness of replacing the split strategy for the state-of-the-art online tree learner, Hoeffding Tree, with a rigorous but more eager splitting strategy that we had previously published as Hoeffding AnyTime Tree.

Emergent and Unspecified Behaviors in Streaming Decision Trees

no code implementations • 16 Oct 2020 • Chaitanya Manapragada, Geoffrey I Webb, Mahsa Salehi, Albert Bifet

Hoeffding trees are the state-of-the-art methods in decision tree learning for evolving data streams.

Extremely Fast Decision Tree

no code implementations • 24 Feb 2018 • Chaitanya Manapragada, Geoff Webb, Mahsa Salehi

We introduce a novel incremental decision tree learning algorithm, Hoeffding Anytime Tree, that is statistically more efficient than the current state-of-the-art, Hoeffding Tree.

General Classification