Högskolan i Skövde

his.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • apa-cv
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
On Evidential Combination Rules for Ensemble Classifiers
University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre.ORCID iD: 0000-0001-8382-0300
University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre.
University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre.ORCID iD: 0000-0003-2973-3112
2008 (English)In: Proceedings of the 11th International Conference on Information Fusion: Cologne, Germany, June 30 - July 03, 2008, IEEE, 2008, p. 553-560Conference paper, Published paper (Refereed)
Abstract [en]

Ensemble classifiers are known to generally perform better than each individual classifier of which they consist. One approach to classifier fusion is to apply Shafer’s theory of evidence. While most approaches have adopted Dempster’s rule of combination, a multitude of combination rules have been proposed. A number of combination rules as well as two voting rules are compared when used in conjunction with a specific kind of ensemble classifier, known as random forests, w.r.t. accuracy, area under ROC curve and Brier score on 27 datasets. The empirical evaluation shows that the choice of combination rule can have a significant impact on the performance for a single dataset, but in general the evidential combination rules do not perform better than the voting rules for this particular ensemble design. Furthermore, among the evidential rules, the associative ones appear to have better performance than the non-associative.

Place, publisher, year, edition, pages
IEEE, 2008. p. 553-560
Keywords [en]
Ensemble classifiers, random forests, evidence theory, Dempster-Shafer theory, combination rules
Research subject
Technology
Identifiers
URN: urn:nbn:se:his:diva-3606Scopus ID: 2-s2.0-56749142942ISBN: 978-3-00-024883-2 (electronic)ISBN: 978-3-8007-3092-6 (print)OAI: oai:DiVA.org:his-3606DiVA, id: diva2:291094
Conference
11th International Conference on Information Fusion, FUSION 2008, Cologne, 30 June 2008 through 3 July 2008, IEEE Catalog Number CFP08FUS, INSPEC Accession Number: 10365759, Code 74110
Note

10.1109/ICIF.2008.4632259

Available from: 2010-01-29 Created: 2010-01-29 Last updated: 2020-08-11Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Scopushttps://ieeexplore.ieee.org/document/4632259

Authority records

Boström, HenrikJohansson, RonnieKarlsson, Alexander

Search in DiVA

By author/editor
Boström, HenrikJohansson, RonnieKarlsson, Alexander
By organisation
School of Humanities and InformaticsThe Informatics Research Centre

Search outside of DiVA

GoogleGoogle Scholar

isbn
urn-nbn

Altmetric score

isbn
urn-nbn
Total: 1472 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • apa-cv
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf