Efficient Algorithms for Decision Tree Cross-validation

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Efficient Algorithms for Decision Tree Cross-validation
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.
Hendrik Blockeel, Jan Struyf
Added 22 Dec 2010
Updated 22 Dec 2010
Type Journal
Year 2002
Where JMLR
Authors Hendrik Blockeel, Jan Struyf
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