Model selection in omnivariate decision trees

Authors:
Olcay Taner Yıldız;Ethem Alpaydın
Affiliations:
Department of Computer Engineering, Boğaziçi University, Istanbul, Turkey;Department of Computer Engineering, Boğaziçi University, Istanbul, Turkey
Venue:
ECML'05 Proceedings of the 16th European conference on Machine Learning
Year:
2005

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Abstract

We propose an omnivariate decision tree architecture which contains univariate, multivariate linear or nonlinear nodes, matching the complexity of the node to the complexity of the data reaching that node. We compare the use of different model selection techniques including AIC, BIC, and CV to choose between the three types of nodes on standard datasets from the UCI repository and see that such omnivariate trees with a small percentage of multivariate nodes close to the root generalize better than pure trees with the same type of node everywhere. CV produces simpler trees than AIC and BIC without sacrificing from expected error. The only disadvantage of CV is its longer training time.