Extremely Fast Decision Tree

Abstract

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. We demonstrate that an implementation of Hoeffding Anytime Tree-"Extremely Fast Decision Tree", a minor modification to the MOA implementation of Hoeffding Tree-obtains significantly superior prequential accuracy on most of the largest classification datasets from the UCI repository. Hoeffding Anytime Tree produces the asymptotic batch tree in the limit, is naturally resilient to concept drift, and can be used as a higher accuracy replacement for Hoeffding Tree in most scenarios, at a small additional computational cost.

Memory Efficient Experience Replay for Streaming Learning

“…While almost no work has been done on streaming classification with DNNs, there are streaming classifiers. Many are based on Hoeffding Decision Trees [6], [18], [25], [36], [37], [38], [39], [56], [63] or ensemble methods [7], [9], [27], [44], [46], [51], [54], [61], [70], [72]. Both approaches are slow to train [24].…”