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Virtual attribute subsetting

conference contribution
posted on 2023-05-23, 03:36 authored by Horton, MP, Cameron-Jones, RM, Williams, RN
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.

History

Publication title

AI 2006: Advances in Artificial Intelligence 19th Australian Joint Conference on Artificial Intelligence

Volume

19

Editors

A Sattar & B Kang

Pagination

214-223

ISBN

3-540-49787-0

Department/School

School of Information and Communication Technology

Publisher

Springer-Verlag

Place of publication

Berlin, Germany

Event title

AJCAI

Event Venue

Hobart, Tasmania

Date of Event (Start Date)

2006-12-04

Date of Event (End Date)

2006-12-08

Rights statement

Copyright 2006 Springer-Verlag Berlin Heidelberg

Repository Status

  • Restricted

Socio-economic Objectives

Other information and communication services not elsewhere classified

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