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dc.contributor.authorSchetinin, Vitaly
dc.contributor.authorPartridge, Derek
dc.contributor.authorKrzanowski, Wojtek J.
dc.contributor.authorEverson, Richard M.
dc.contributor.authorFieldsend, Jonathan E.
dc.contributor.authorBailey, Trevor C.
dc.contributor.authorHernandez, Adolfo
dc.date.accessioned2013-07-09T12:56:27Z
dc.date.issued2004
dc.description.abstractIn 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.en_GB
dc.identifier.citationVol. 3177, pp. 726-732en_GB
dc.identifier.doi10.1007/978-3-540-28651-6_108
dc.identifier.urihttp://hdl.handle.net/10871/11601
dc.language.isoenen_GB
dc.publisherSpringer Berlin Heidelbergen_GB
dc.relation.urlhttp://dx.doi.org/10.1007/978-3-540-28651-6_108en_GB
dc.titleExperimental Comparison of Classification Uncertainty for Randomised and Bayesian Decision Tree Ensemblesen_GB
dc.typeArticleen_GB
dc.typeConference paperen_GB
dc.date.available2013-07-09T12:56:27Z
dc.identifier.isbn9783540228813
dc.identifier.isbn9783540286516
dc.identifier.issn0302-9743
pubs.declined2016-03-07T12:01:42.964+0000
pubs.deleted2016-03-07T12:01:43.167+0000
dc.descriptionCopyright © 2004 Springer-Verlag Berlin Heidelberg. The final publication is available at link.springer.comen_GB
dc.descriptionBook title: Intelligent Data Engineering and Automated Learning – IDEAL 2004en_GB
dc.description5th International Conference on Intelligent Data Engineering and Automated Learning – IDEAL 2004, Exeter, UK. August 25-27, 2004en_GB
dc.identifier.journalLecture Notes in Computer Scienceen_GB


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