Extremely fast decision tree

Chaitanya Manapragada, Geoffrey I. Webb, Mahsa Salehi

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

22 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationKDD' 2018 - Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Subtitle of host publicationAugust 19-23, 2018, London, United Kingdom
EditorsChih-Jen Lin, Hui Xiong
Place of PublicationNew York NY USA
PublisherAssociation for Computing Machinery (ACM)
Number of pages10
ISBN (Electronic)9781450355520
Publication statusPublished - 2018
EventACM International Conference on Knowledge Discovery and Data Mining 2018 - London, United Kingdom
Duration: 19 Aug 201823 Aug 2018
Conference number: 24th
http://www.kdd.org/kdd2018/ (Conference website)


ConferenceACM International Conference on Knowledge Discovery and Data Mining 2018
Abbreviated titleKDD 2018
CountryUnited Kingdom
Internet address


  • Classification
  • Decision Trees
  • Incremental Learning

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