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FLAIRS
2001

Hybrid Decision Tree Learners with Alternative Leaf Classifiers: An Empirical Study

13 years 5 months ago
Hybrid Decision Tree Learners with Alternative Leaf Classifiers: An Empirical Study
Therehasbeensurprisinglylittle researchso far that systematicallyinvestigatedthe possibilityof constructinghybrid learningalgorithmsbysimplelocal modificationsto decision tree learners. In this paperweanalyzethree variantsof a C4.5-stylelearner, introducingalternativeleaf models(Naive Bayes,IBI, and multi-responselinear regression, respectively) whichcanreplacethe originalC4.5leaf nodesduring reducederror post-pruning.Weempiricallyshowthat these simplemodificationscan improveuponthe performanceof the original decisiontree algorithmandevenuponbothconstituent algorithms.Wesee this as a step towardsthe constructionof learnersthatlocallyoptimizetheir biasfor different regionsof the instancespace.
Alexander K. Seewald, Johann Petrak, Gerhard Widme
Added 31 Oct 2010
Updated 31 Oct 2010
Type Conference
Year 2001
Where FLAIRS
Authors Alexander K. Seewald, Johann Petrak, Gerhard Widmer
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