An experimental study on ensembles of functional trees

Authors:
Juan J. Rodríguez;César García-Osorio;Jesús Maudes;José Francisco Díez-Pastor
Affiliations:
University of Burgos, Spain;University of Burgos, Spain;University of Burgos, Spain;University of Burgos, Spain
Venue:
MCS'10 Proceedings of the 9th international conference on Multiple Classifier Systems
Year:
2010

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Abstract

Functional Trees are one type of multivariate trees. This work studies the performance of different ensemble methods (Bagging, Random Subspaces, AdaBoost, Rotation Forest) using three variants (multivariate internal nodes, multivariate leaves or both) of these trees as base classifiers. The best results, for all the ensemble methods, are obtained using Functional Trees with multivariate leaves and univariate internal nodes. The best overall configuration is obtained with Rotation Forest. Ensembles of Functional Trees are compared to ensembles of univariate Decision Trees, being the results favourable for the variant of Functional Trees with univariate internal nodes and multivariate leaves. Kappa-error diagrams are used to study the diversity and accuracy of the base classifiers.