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

Robert Cox, David Clark, Alice Richardson

Research output: A Conference proceeding or a Chapter in BookConference contribution

4 Citations (Scopus)

Abstract

If 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.

Original languageEnglish
Title of host publicationAdvanced Topics in Artificial Intelligence - 12th Australian Joint Conference on Artificial Intelligence, AI 1999, Proceedings
EditorsNorman Foo
Place of PublicationNetherlands
PublisherSpringer
Pages268-277
Number of pages10
ISBN (Print)9783540668220
DOIs
Publication statusPublished - 1 Jan 1999
Event12th Australian Joint Conference on Artificial Intelligence, AI 1999 - Sydney, Australia
Duration: 6 Dec 199910 Dec 1999

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume1747
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference12th Australian Joint Conference on Artificial Intelligence, AI 1999
CountryAustralia
CitySydney
Period6/12/9910/12/99

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