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      • University of Waikato Research
      • Computing and Mathematical Sciences
      • Computing and Mathematical Sciences Papers
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      Stress- testing Hoeffding trees

      Holmes, Geoffrey; Kirkby, Richard Brendon; Pfahringer, Bernhard
      DOI
       10.1007/11564126_50
      Link
       www.springerlink.com
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      Citation
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      Holmes, G., Kirkby, R. & Pfahringer, B. (2005). Stress- testing Hoeffding trees . In A. Jorge et al. (Eds.), Proceedings of the 9th European Conference on Principles and Practice of Knowledge Discovery in Databases, Porto, Portugal, October 3-7, 2005. (pp. 495-502). Berlin: Springer.
      Permanent Research Commons link: https://hdl.handle.net/10289/1460
      Abstract
      Hoeffding trees are state-of-the-art in classification for data streams. They perform prediction by choosing the majority class at each leaf. Their predictive accuracy can be increased by adding Naive Bayes models at the leaves of the trees. By stress-testing these two prediction methods using noise and more complex concepts and an order of magnitude more instances than in previous studies, we discover situations where the Naive Bayes method outperforms the standard Hoeffding tree initially but is eventually overtaken. The reason for this crossover is determined and a hybrid adaptive method is proposed that generally outperforms the two original prediction methods for both simple and complex concepts as well as under noise.
      Date
      2005
      Type
      Conference Contribution
      Publisher
      Springer, Berlin
      Collections
      • Computing and Mathematical Sciences Papers [1455]
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