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Stress- testing Hoeffding trees

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.
Type
Conference Contribution
Type of thesis
Series
Citation
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.
Date
2005
Publisher
Springer, Berlin
Degree
Supervisors
Rights
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