Experimental Comparison of Classification Uncertainty for Randomised and Bayesian Decision Tree Ensembles
Schetinin, Vitaly; Partridge, Derek; Krzanowski, Wojtek J.; et al.Everson, Richard M.; Fieldsend, Jonathan E.; Bailey, Trevor C.; Hernandez, Adolfo
Date: 13 August 2004
Conference paper
Publisher
Springer Berlin Heidelberg
Publisher DOI
Abstract
In 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 ...
In 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.
Computer Science
Faculty of Environment, Science and Economy
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