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Virtual attribute subsetting

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Horton, M and Cameron-Jones, RM and Williams, R (2006) Virtual attribute subsetting. In: 19th Australian Joint Conference on Artificial Intelligence, 04-08 Dec 2006, Hobart, Australia.

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Abstract

Attribute subsetting is a meta-classification technique, based on learning multiple base-level classifiers on projections of the training data. In prior work with nearest-neighbour base classifiers, attribute subsetting was modified to learn only one classifier, then to selectively ignore attributes at classification time to generate multiple predictions. In this paper, the approach is generalized to any type of base classifier. This 'virtual attribute subsetting' requires a fast subset choice algorithm; one such algorithm is found and described. In tests with three different base classifier types, virtual attribute subsetting is shown to yield some or all of the benefits of standard attribute subsetting while reducing training time and storage requirements.

Item Type: Conference or Workshop Item (Paper)
Keywords: Machine learning, Classifier learning, Meta-classification
Date Deposited: 29 Nov 2006
Last Modified: 18 Nov 2014 03:12
URI: http://eprints.utas.edu.au/id/eprint/440
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