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On the Use of Accuracy and Diversity Measures for Evaluating and Selecting Ensembles of Classifiers
University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre.
2008 (English)In: Proceedindgs of the Seventh International Conference on Machine Learning and Applications, IEEE Computer Society, 2008, 127-132 p.Conference paper, (Refereed)
Abstract [en]

 The test set accuracy for ensembles of classifiers selected based on single measures of accuracy and diversity as well as combinations of such measures is investigated. It is found that by combining measures, a higher test set accuracy may be obtained than by using any single accuracy or diversity measure. It is further investigated whether a multi-criteria search for an ensemble that maximizes both accuracy and diversity leads to more accurate ensembles than by optimizing a single criterion. The results indicate that it might be more beneficial to search for ensembles that are both accurate and diverse. Furthermore, the results show that diversity measures could compete with accuracy measures as selection criterion.

 

 

Place, publisher, year, edition, pages
IEEE Computer Society, 2008. 127-132 p.
Research subject
Technology
Identifiers
URN: urn:nbn:se:his:diva-2580DOI: 10.1109/ICMLA.2008.102ISI: 000263205800018Scopus ID: 2-s2.0-60649083359ISBN: 978-0-7695-3495-4 OAI: oai:DiVA.org:his-2580DiVA: diva2:139382
Conference
7th International Conference on Machine Learning and Applications, ICMLA 2008;San Diego, CA;11 December 2008through13 December 2008
Available from: 2009-01-22 Created: 2009-01-22 Last updated: 2013-03-17

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CiteExportLink to record
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Citation style
  • apa
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  • modern-language-association-8th-edition
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Language
  • de-DE
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