Virtual attribute subsetting

Authors:
Michael Horton;Mike Cameron-Jones;Ray Williams
Affiliations:
School of Computing, University of Tasmania, TAS, Australia;School of Computing, University of Tasmania, TAS, Australia;School of Computing, University of Tasmania, TAS, Australia
Venue:
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
Year:
2006

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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.