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
Increasing rule extraction accuracy by post-processing GP trees
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))
2008 (English)In: 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence), IEEE, 2008, p. 3005-3010Conference paper, Published paper (Refereed)
Abstract [en]

Genetic programming (GP), is a very general and efficient technique, often capable of outperforming more specialized techniques on a variety of tasks. In this paper, we suggest a straightforward novel algorithm for post-processing of GP classification trees. The algorithm iteratively, one node at a time, searches for possible modifications that would result in higher accuracy. More specifically, the algorithm for each split evaluates every possible constant value and chooses the best. With this design, the post-processing algorithm can only increase training accuracy, never decrease it. In this study, we apply the suggested algorithm to GP trees, extracted from neural network ensembles. Experimentation, using 22 UCI datasets, shows that the post-processing results in higher test set accuracies on a large majority of datasets. As a matter of fact, for two setups of three evaluated, the increase in accuracy is statistically significant.

Place, publisher, year, edition, pages
IEEE, 2008. p. 3005-3010
Series
IEEE Transactions on Evolutionary Computation, ISSN 1089-778X, E-ISSN 1941-0026
National Category
Computer Sciences Information Systems
Research subject
Technology
Identifiers
URN: urn:nbn:se:his:diva-3612DOI: 10.1109/CEC.2008.4631203ISI: 000263406501201Scopus ID: 2-s2.0-55749096880ISBN: 978-1-4244-1823-7 (electronic)ISBN: 978-1-4244-1822-0 (print)OAI: oai:DiVA.org:his-3612DiVA, id: diva2:291121
Conference
2008 IEEE Congress on Evolutionary Computation, CEC 2008, Hong Kong, China, 1-6 June 2008
Note

© 2008 IEEE

Available from: 2010-01-29 Created: 2010-01-29 Last updated: 2021-03-02Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopusFulltext (DiVA Högskolan i Borås)

Authority records

Niklasson, Lars

Search in DiVA

By author/editor
Niklasson, 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: 516 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