Title: Efficient algorithms for decision tree cross-validation
Authors: Blockeel, Hendrik ×
Struyf, Jan #
Issue Date: 2002
Publisher: MIT Press
Series Title: Journal of Machine Learning Research vol:3 issue:Dec pages:621-650
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. We identify a number of parameters that influence the obtainable speedups, and validate and refine our analysis with experiments on a variety of data sets with two different implementations. Besides cross-validation, we also briefly explore the usefulness of these techniques for bagging. We conclude with some guidelines concerning when these optimizations should be considered.
ISSN: 1532-4435
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Informatics Section
× corresponding author
# (joint) last author

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