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
Genetically Evolved Trees Representing Ensembles
School of Business and Informatics, University of Borås, Sweden.
School of Business and Informatics, University of Borås, Sweden.
School of Business and Informatics, University of Borås, Sweden.
University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre. (Skövde Cognition and Artificial Intelligence Lab (SCAI))
2006 (English)In: Artificial Intelligence and Soft Computing – ICAISC 2006: 8th International Conference, Zakopane, Poland, June 25-29, 2006. Proceedings / [ed] Leszek Rutkowski, Ryszard Tadeusiewicz, Lotfi A. Zadeh, Jacek M. Żurada, Springer, 2006, p. 613-622Conference paper, Published paper (Refereed)
Abstract [en]

We have recently proposed a novel algorithm for ensemble creation called GEMS (Genetic Ensemble Member Selection). GEMS first trains a fixed number of neural networks (here twenty) and then uses genetic programming to combine these networks into an ensemble. The use of genetic programming makes it possible for GEMS to not only consider ensembles of different sizes, but also to use ensembles as intermediate building blocks. In this paper, which is the first extensive study of GEMS, the representation language is extended to include tests partitioning the data, further increasing flexibility. In addition, several micro techniques are applied to reduce overfitting, which appears to be the main problem for this powerful algorithm. The experiments show that GEMS, when evaluated on 15 publicly available data sets, obtains very high accuracy, clearly outperforming both straightforward ensemble designs and standard decision tree algorithms.

Place, publisher, year, edition, pages
Springer, 2006. p. 613-622
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 4029
National Category
Computer Sciences Information Systems
Research subject
Technology
Identifiers
URN: urn:nbn:se:his:diva-1587DOI: 10.1007/11785231_64ISI: 000239600000064Scopus ID: 2-s2.0-33746239343ISBN: 978-3-540-35748-3 (print)ISBN: 978-3-540-35750-6 (electronic)ISBN: 3-540-35748-3 (print)OAI: oai:DiVA.org:his-1587DiVA, id: diva2:31863
Conference
Artificial Intelligence and Soft Computing – ICAISC 2006, 8th International Conference, Zakopane, Poland, June 25-29, 2006
Note

Also part of the Lecture Notes in Artificial Intelligence book sub series (LNAI, volume 4029)

Available from: 2008-02-08 Created: 2008-02-08 Last updated: 2021-04-22Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records

Löfström, TuveKönig, RikardNiklasson, Lars

Search in DiVA

By author/editor
Löfström, TuveKönig, RikardNiklasson, Lars
By organisation
School of Humanities and InformaticsThe Informatics Research Centre
Computer SciencesInformation Systems

Search outside of DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 526 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