Virtual Attribute Subsetting

  • Michael Horton
  • Mike Cameron-Jones
  • Ray Williams
Conference paper

DOI: 10.1007/11941439_25

Part of the Lecture Notes in Computer Science book series (LNCS, volume 4304)
Cite this paper as:
Horton M., Cameron-Jones M., Williams R. (2006) Virtual Attribute Subsetting. In: Sattar A., Kang B. (eds) AI 2006: Advances in Artificial Intelligence. AI 2006. Lecture Notes in Computer Science, vol 4304. Springer, Berlin, Heidelberg

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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Michael Horton
    • 1
  • Mike Cameron-Jones
    • 1
  • Ray Williams
    • 1
  1. 1.School of ComputingUniversity of TasmaniaAustralia

Personalised recommendations