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AUSAI
2006
Springer

Virtual Attribute Subsetting

9 years 9 months ago
Virtual Attribute Subsetting
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.
Michael Horton, R. Mike Cameron-Jones, Raymond Wil
Added 20 Aug 2010
Updated 20 Aug 2010
Type Conference
Year 2006
Where AUSAI
Authors Michael Horton, R. Mike Cameron-Jones, Raymond Williams
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