his.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • 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
Genetically Evolved kNN Ensembles
School of Business and Informatics, University of Borås, Borås, Sweden.
School of Business and Informatics, University of Borås, Borås, Sweden.
University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre.
2009 (English)In: Data Mining: Special Issue in Annals of Information Systems / [ed] Robert Stahlbock, Sven F. Crone, Stefan Lessmann, Springer Science+Business Media B.V., 2009, 1, p. 299-313Chapter in book (Other academic)
Abstract [en]

Both theory and a wealth of empirical studies have established that ensembles are more accurate than single predictive models. For the ensemble approach to work, base classifiers must not only be accurate but also diverse, i.e., they should commit their errors on different instances. Instance-based learners are, however, very robust with respect to variations of a data set, so standard resampling methods will normally produce only limited diversity. Because of this, instance-based learners are rarely used as base classifiers in ensembles. In this chapter, we introduce a method where genetic programming is used to generate kNN base classifiers with optimized k-values and feature weights. Due to the inherent inconsistency in genetic programming (i.e., different runs using identical data and parameters will still produce different solutions) a group of independently evolved base classifiers tend to be not only accurate but also diverse. In the experimentation, using 30 data sets from the UCI repository, two slightly different versions of kNN ensembles are shown to significantly outperform both the corresponding base classifiers and standard kNN with optimized k-values, with respect to accuracy and AUC.

Place, publisher, year, edition, pages
Springer Science+Business Media B.V., 2009, 1. p. 299-313
Series
Annals of Information Systems, ISSN 1934-3221 ; 8
National Category
Computer and Information Sciences
Research subject
Technology
Identifiers
URN: urn:nbn:se:his:diva-3839DOI: 10.1007/978-1-4419-1280-0_13ISBN: 978-1-4419-1279-4 ISBN: 978-1-4419-1280-0 OAI: oai:DiVA.org:his-3839DiVA, id: diva2:307386
Available from: 2010-04-01 Created: 2010-04-01 Last updated: 2018-01-12Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full text

Authority records BETA

König, RikardNiklasson, Lars

Search in DiVA

By author/editor
König, RikardNiklasson, Lars
By organisation
School of Humanities and InformaticsThe Informatics Research Centre
Computer and Information Sciences

Search outside of DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 352 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • 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