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G-REX: A Versatile Framework for Evolutionary Data Mining
University of Borås, Sweden.
University of Borås, Sweden.
University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre.
2008 (English)In: Proceedings - IEEE International Conference on Data Mining Workshops, ICDM Workshops 2008, IEEE Computer Society, 2008, p. 971-974Conference paper, Published paper (Refereed)
Abstract [en]

 

This paper presents G-REX, a versatile data mining framework based on Genetic Programming. What differs G-REX from other GP frameworks is that it doesn’t strive to be a general purpose framework. This allows G-REX to include more functionality specific to data mining like preprocessing, evaluation- and optimization methods, but also a multitude of predefined classification and regression models. Examples of predefined models are decision trees, decision lists, k-NN with attribute weights, hybrid kNN-rules, fuzzy-rules and several different regression models. The main strength is, however, the flexibility, making it easy to modify, extend and combine all of the predefined functionality. G-REX is, in addition, available in a special Weka package adding useful evolutionary functionality to the standard data mining tool Weka.

 

 

Place, publisher, year, edition, pages
IEEE Computer Society, 2008. p. 971-974
Research subject
Technology
Identifiers
URN: urn:nbn:se:his:diva-2829DOI: 10.1109/ICDMW.2008.117Scopus ID: 2-s2.0-62449143933ISBN: 978-0-7695-3503-6 OAI: oai:DiVA.org:his-2829DiVA, id: diva2:201484
Conference
IEEE International Conference on Data Mining Workshops, ICDM Workshops 2008;Pisa;15 December 2008 through 19 December 2008
Available from: 2009-03-04 Created: 2009-03-04 Last updated: 2017-11-27

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Niklasson, Lars

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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