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

Alexander K. Seewald and Johann Petrak, Austrian Research Institute for Artificial Intelligence, Austria; Gerhard Widmer, Austrian Research Institute for Artificial Intelligence and University of Vienna, Austria

There has been surprisingly little research so far that systematically investigated the possibility of constructing hybrid learning algorithms by simple local modifications to decision tree learners. In this paper we analyze three variants of a C4.5-style learner, introducing alternative leaf models (Naive Bayes, IB1, and multi-response linear regression, respectively) which can replace the original C4.5 leaf nodes during reduced error post-pruning. We empirically show that these simple modifications can improve upon the performance of the original decision tree algorithm and even upon both constituent algorithms. We see this as a step towards the construction of learners that locally optimize their bias for different regions of the instance space.

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