eCite Digital Repository

Virtual attribute subsetting


Horton, MP and Cameron-Jones, RM and Williams, RN, Virtual attribute subsetting, AI 2006: Advances in Artificial Intelligence 19th Australian Joint Conference on Artificial Intelligence, 4-8 December, 2006, Hobart, Tasmania, pp. 214-223. ISBN 3-540-49787-0 (2006) [Refereed Conference Paper]

Copyright Statement

Copyright 2006 Springer-Verlag Berlin Heidelberg

DOI: doi:10.1007/11941439_25


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 Details

Item Type:Refereed Conference Paper
Research Division:Information and Computing Sciences
Research Group:Artificial intelligence
Research Field:Artificial life and complex adaptive systems
Objective Division:Information and Communication Services
Objective Group:Other information and communication services
Objective Field:Other information and communication services not elsewhere classified
UTAS Author:Horton, MP (Mr Michael Horton)
UTAS Author:Cameron-Jones, RM (Dr Mike Cameron-Jones)
UTAS Author:Williams, RN (Dr Ray Williams)
ID Code:41302
Year Published:2006
Deposited By:Computing
Deposited On:2006-08-01
Last Modified:2012-11-06

Repository Staff Only: item control page