Endre søk
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Genetically Evolved Nearest Neighbor Ensembles
Högskolan i Borås, Institutionen Handels- och IT-högskolan. (CSL@BS)
Högskolan i Borås, Institutionen Handels- och IT-högskolan. (CSL@BS)
2009 (engelsk)Inngår i: Data Mining: Special Issue in Annals of Information Systems / [ed] Robert Stahlbock, Stefan Lessmann, Sven F. Crone, Springer Verlag , 2009, s. 299-313Kapittel i bok, del av antologi (Fagfellevurdert)
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 dataset, 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 paper, 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 datasets 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.

sted, utgiver, år, opplag, sider
Springer Verlag , 2009. s. 299-313
Serie
Annals of Information Systems, ISSN 1934-3221 ; 8
Emneord [en]
knn, ensembles, genetic programming, Machine learning
Emneord [sv]
data mining
HSV kategori
Identifikatorer
URN: urn:nbn:se:hb:diva-4925DOI: 10.1007/978-1-4419-1280-0Lokal ID: 2320/5722ISBN: 978-1-4419-1279-4 (tryckt)ISBN: 978-1-4419-1280-0 (tryckt)OAI: oai:DiVA.org:hb-4925DiVA, id: diva2:884343
Tilgjengelig fra: 2015-12-17 Laget: 2015-12-17 Sist oppdatert: 2018-01-10bibliografisk kontrollert

Open Access i DiVA

Fulltekst mangler i DiVA

Andre lenker

Forlagets fullteksthttp://www.mendeley.com/download/public/839011/2052332402/a71766fe7536e49a81601ffbfca56b127e1d5901/dl.pdf

Personposter BETA

Johansson, UlfKönig, Rikard

Søk i DiVA

Av forfatter/redaktør
Johansson, UlfKönig, Rikard
Av organisasjonen

Søk utenfor DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric

doi
isbn
urn-nbn
Totalt: 92 treff
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf