AI 2006: Advances in Artificial Intelligence

Volume 4304 of the series Lecture Notes in Computer Science pp 214-223

Virtual Attribute Subsetting

  • Michael HortonAffiliated withSchool of Computing, University of Tasmania
  • , Mike Cameron-JonesAffiliated withSchool of Computing, University of Tasmania
  • , Ray WilliamsAffiliated withSchool of Computing, University of Tasmania

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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.