Title: Efficient algorithms for decision tree cross-validation
Authors: Blockeel, Hendrik ×
Struyf, Jan #
Issue Date: 2001
Publisher: Morgan Kaufmann
Host Document: Proceedings of the Eighteenth International Conference on Machine Learning pages:11-18
Conference: International Conference on Machine Learning edition:18 location:Williamstown, Massachusetts date:June 28 - July 1, 2001
Abstract: Cross-validation is a useful and generally applicable technique often employed in machine learning, including decision tree induction. An important disadvantage of straightforward implementation of the technique is its computational overhead. In this paper we show that, for decision trees, the computational overhead of cross-validation can be reduced significantly by integrating the cross-validation with the normal decision tree induction process. We discuss how existing decision tree algorithms can be adapted to this aim, and provide an analysis of the speedups these adaptations may yield. The analysis is supported by experimental results.
Publication status: published
KU Leuven publication type: IC
Appears in Collections:Informatics Section
× corresponding author
# (joint) last author

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