Probabilistic Hoeffding Trees

Sped-Up Convergence and Adaption of Online Trees on Changing Data Streams
  • Jonathan BoidolEmail author
  • Andreas Hapfelmeier
  • Volker Tresp
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9165)


Increasingly, data streams are generated from a growing number of small, cheap sensors that monitor, e.g., personal activities, industrial facilities or the natural environment. In these settings, there are often rapid changes in input-to-target relations and we are concerned with tree-structured models that can rapidly adapt to these changes. Based on our new algorithms accuracy and tracking behavior is improved, which we demonstrate for a number of popular tree based-classifiers with over state-of-the-art change detection using five data sets and two different settings. The key novel idea is the representation of record values as distributions rather than point-values in the stream setting, covering a larger part of the instance space early on, and resulting in an often smaller, more flexible classification model.


Online decision tree learning Uncertainty-aware data streams Classification Concept change Regularization 


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Jonathan Boidol
    • 1
    • 2
    Email author
  • Andreas Hapfelmeier
    • 2
  • Volker Tresp
    • 1
    • 2
  1. 1.Institute for Computer ScienceLudwig-Maximilians UniversityMünchenGermany
  2. 2.Siemens AG, Corporate TechnologyMünchenGermany

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