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research
Comparison of the Bayesian and Randomised Decision Tree Ensembles within an Uncertainty Envelope Technique
Authors
Trevor C. Bailey
Richard M. Everson
+5 more
Jonathan E. Fieldsend
Adolfo Hernandez
Wojtek J. Krzanowski
Derek Partridge
Vitaly Schetinin
Publication date
29 April 2015
Publisher
'Springer Science and Business Media LLC'
Doi
Cite
Abstract
Copyright © 2006 Springer. The final publication is available at link.springer.comMultiple Classifier Systems (MCSs) allow evaluation of the uncertainty of classification outcomes that is of crucial importance for safety critical applications. The uncertainty of classification is determined by a trade-off between the amount of data available for training, the classifier diversity and the required performance. The interpretability of MCSs can also give useful information for experts responsible for making reliable classifications. For this reason Decision Trees (DTs) seem to be attractive classification models for experts. The required diversity of MCSs exploiting such classification models can be achieved by using two techniques, the Bayesian model averaging and the randomised DT ensemble. Both techniques have revealed promising results when applied to real-world problems. In this paper we experimentally compare the classification uncertainty of the Bayesian model averaging with a restarting strategy and the randomised DT ensemble on a synthetic dataset and some domain problems commonly used in the machine learning community. To make the Bayesian DT averaging feasible, we use a Markov Chain Monte Carlo technique. The classification uncertainty is evaluated within an Uncertainty Envelope technique dealing with the class posterior distribution and a given confidence probability. Exploring a full posterior distribution, this technique produces realistic estimates which can be easily interpreted in statistical terms. In our experiments we found out that the Bayesian DTs are superior to the randomised DT ensembles within the Uncertainty Envelope technique
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Last time updated on 03/08/2016