Cultural advice

The Australian National University acknowledges, celebrates and pays our respects to the Ngunnawal and Ngambri people of the Canberra region and to all First Nations Australians on whose traditional lands we meet and work, and whose cultures are among the oldest continuing cultures in human history.

Aboriginal and Torres Strait Islander peoples are advised that ANU Library collections may include images, names, voices, and other representations of deceased persons.

Material in the collection may contain terms, language or views that reflect the period in which the item was created and may be considered inappropriate today.

An investigation into the effect of ensemble size and voting threshold on the accuracy of neural network ensembles

Simple item page
dc.contributor.authorCox, Roberten
dc.contributor.authorClark, Daviden
dc.contributor.authorRichardson, Aliceen
dc.date.accessioned2026-01-01T12:42:08Z
dc.date.available2026-01-01T12:42:08Z
dc.date.issued1999en
dc.description.abstractIf voting is used by an ensemble to classify data, some data points may not be classified, but a higher proportion of those which are classified are classified correctly. This trade off is affected by ensemble size and voting threshold. This paper investigates the effect of ensemble size on the proportions of decisions made and correct decisions. It does this for majority voting and consensus voting on ensembles of neural network classifiers constructed using bagging. It also models the relationships in order to estimate the asymptotic values as the ensemble size increases.en
dc.description.statusPeer-revieweden
dc.format.extent10en
dc.identifier.isbn3540668225en
dc.identifier.isbn9783540668220en
dc.identifier.issn0302-9743en
dc.identifier.otherORCID:/0000-0001-7084-1524/work/162951189en
dc.identifier.scopus84957633680en
dc.identifier.urihttps://hdl.handle.net/1885/733800397
dc.language.isoenen
dc.publisherSpringer Verlagen
dc.relation.ispartofAdvanced Topics in Artificial Intelligence - 12th Australian Joint Conference on Artificial Intelligence, AI 1999, Proceedingsen
dc.relation.ispartofseries12th Australian Joint Conference on Artificial Intelligence, AI 1999en
dc.relation.ispartofseriesLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)en
dc.rightsPublisher Copyright: © Springer-Verlag Berlin Heidelberg 1999.en
dc.titleAn investigation into the effect of ensemble size and voting threshold on the accuracy of neural network ensemblesen
dc.typeConference paperen
dspace.entity.typePublicationen
local.bibliographicCitation.lastpage277en
local.bibliographicCitation.startpage268en
local.contributor.affiliationCox, Robert; University of Canberraen
local.contributor.affiliationClark, David; University of Canberraen
local.contributor.affiliationRichardson, Alice; The Australian National Universityen
local.identifier.doi10.1007/3-540-46695-9_23en
local.identifier.essn1611-3349en
local.identifier.pure48dfb7fd-3116-49a1-8cd2-e4f049752863en
local.identifier.urlhttps://www.scopus.com/pages/publications/84957633680en
local.type.statusPublisheden

Downloads

Collections

ANU Research Publications

DSpace software copyright © 2002-2026 LYRASIS