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The Discovery and Use of Ordinal Information on Attribute Values in Classifier Learning


Berry, A and Cameron-Jones, R, The Discovery and Use of Ordinal Information on Attribute Values in Classifier Learning, Proceedings of AI2011: Advances in Artificial Intelligence, the 24th Australasian Joint Conference, 5-8 December 2011, Perth, Australia, pp. 31-40. ISBN 978-3-642-25831-2 (2011) [Refereed Conference Paper]

Copyright Statement

Copyright 2011 Springer-Verlag

DOI: doi:10.1007/978-3-642-25832-9


Rule and tree based classifier learning systems can employ the idea of order on discrete attribute and class values to aid in classifi- cation. Much work has been done on using both orders on class values and monotonic relationships between class and attribute orders. In con- trast to this, we examine the usefulness of order specifically on attribute values, and present and evaluate three new methods for recovering or discovering such orders, showing that under some circumstances they can significantly improve accuracy. In addition we introduce the use of classifier ensembles that use random value orders as a source of variation, and show that this can also lead to significant accuracy gains.

Item Details

Item Type:Refereed Conference Paper
Keywords:machine learning, classifier learning, decision trees, ordinal attributes, ensemble classifiers, ordinal aggregation.
Research Division:Information and Computing Sciences
Research Group:Computer vision and multimedia computation
Research Field:Pattern recognition
Objective Division:Expanding Knowledge
Objective Group:Expanding knowledge
Objective Field:Expanding knowledge in the information and computing sciences
UTAS Author:Berry, A (Mr Adam Berry)
UTAS Author:Cameron-Jones, R (Dr Mike Cameron-Jones)
ID Code:75159
Year Published:2011
Deposited By:Information and Communication Technology
Deposited On:2012-01-16
Last Modified:2013-04-25

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