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New Options for Hoeffding Trees

  • Bernhard Pfahringer
  • Geoffrey Holmes
  • Richard Kirkby
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4830)

Abstract

Hoeffding trees are state-of-the-art for processing high-speed data streams. Their ingenuity stems from updating sufficient statistics, only addressing growth when decisions can be made that are guaranteed to be almost identical to those that would be made by conventional batch learning methods. Despite this guarantee, decisions are still subject to limited lookahead and stability issues. In this paper we explore Hoeffding Option Trees, a regular Hoeffding tree containing additional option nodes that allow several tests to be applied, leading to multiple Hoeffding trees as separate paths. We show how to control tree growth in order to generate a mixture of paths, and empirically determine a reasonable number of paths. We then empirically evaluate a spectrum of Hoeffding tree variations: single trees, option trees and bagged trees. Finally, we investigate pruning. We show that on some datasets a pruned option tree can be smaller and more accurate than a single tree.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Bernhard Pfahringer
    • 1
  • Geoffrey Holmes
    • 1
  • Richard Kirkby
    • 1
  1. 1.Department of Computer Science, University of Waikato, HamiltonNew Zealand

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