Experimental Comparison of Classification Uncertainty for Randomised and Bayesian Decision Tree Ensembles

Abstract

Copyright © 2004 Springer-Verlag Berlin Heidelberg. The final publication is available at link.springer.comBook title: Intelligent Data Engineering and Automated Learning – IDEAL 20045th International Conference on Intelligent Data Engineering and Automated Learning – IDEAL 2004, Exeter, UK. August 25-27, 2004In this paper we experimentally compare the classification uncertainty of the randomised Decision Tree (DT) ensemble technique and the Bayesian DT technique with a restarting strategy on a synthetic dataset as well as on some datasets commonly used in the machine learning community. For quantitative evaluation of classification uncertainty, we use an Uncertainty Envelope dealing with the class posterior distribution and a given confidence probability. Counting the classifier outcomes, this technique produces feasible evaluations of the classification uncertainty. Using this technique in our experiments, we found that the Bayesian DT technique is superior to the randomised DT ensemble technique