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Genetically Evolved Nearest Neighbor Ensembles
University of Borås, School of Business and IT. (CSL@BS)
University of Borås, School of Business and IT. (CSL@BS)
2009 (English)In: Data Mining: Special Issue in Annals of Information Systems / [ed] Robert Stahlbock, Stefan Lessmann, Sven F. Crone, Springer Verlag , 2009, p. 299-313Chapter in book (Refereed)
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.

Place, publisher, year, edition, pages
Springer Verlag , 2009. p. 299-313
Series
Annals of Information Systems, ISSN 1934-3221 ; 8
Keywords [en]
knn, ensembles, genetic programming, Machine learning
Keywords [sv]
data mining
National Category
Computer and Information Sciences Computer and Information Sciences
Identifiers
URN: urn:nbn:se:hb:diva-4925DOI: 10.1007/978-1-4419-1280-0Local ID: 2320/5722ISBN: 978-1-4419-1279-4 (print)ISBN: 978-1-4419-1280-0 (print)OAI: oai:DiVA.org:hb-4925DiVA, id: diva2:884343
Available from: 2015-12-17 Created: 2015-12-17 Last updated: 2018-01-10Bibliographically approved

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Publisher's full texthttp://www.mendeley.com/download/public/839011/2052332402/a71766fe7536e49a81601ffbfca56b127e1d5901/dl.pdf

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Johansson, UlfKönig, Rikard

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CiteExportLink to record
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Citation style
  • apa
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Output format
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