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A Study on Class-Specifically Discounted Belief for Ensemble Classifiers
University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre.
University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre.
University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre.
2008 (English)In: Proceedings of the IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2008), IEEE Press, 2008, 614-619 p.Conference paper, (Refereed)
Abstract [en]

Ensemble classifiers are known to generally perform better than their constituent classifiers. Whereas a lot of work has been focusing on the generation of classifiers for ensembles, much less attention has been given to the fusion of individual classifier outputs. One approach to fuse the outputs is to apply Shafer’s theory of evidence, which provides a flexible framework for expressing and fusing beliefs. However, representing and fusing beliefs is non-trivial since it can be performed in a multitude of ways within the evidential framework. In a previous article, we compared different evidential combination rules for ensemble fusion. The study involved a single belief representation which involved discounting (i.e., weighting) the classifier outputs with classifier reliability. The classifier reliability was interpreted as the classifier’s estimated accuracy, i.e., the percentage of correctly classified examples. However, classifiers may have different performance for different classes and in this work we assign the reliability of a classifier output depending on the classspecific reliability of the classifier. Using 27 UCI datasets, we compare the two different ways of expressing beliefs and some evidential combination rules. The result of the study indicates that there is indeed an advantage of utilizing class-specific reliability compared to accuracy in an evidential framework for combining classifiers in the ensemble design considered.

Place, publisher, year, edition, pages
IEEE Press, 2008. 614-619 p.
Keyword [en]
ensemble classifiers, random forests, evidence theory, Dempster-Shafer theory, combination rules
Research subject
Technology
Identifiers
URN: urn:nbn:se:his:diva-3627DOI: 10.1109/MFI.2008.4648012ISI: 000265022100009Scopus ID: 2-s2.0-67650514819ISBN: 978-1-4244-2144-2 OAI: oai:DiVA.org:his-3627DiVA: diva2:291340
Conference
2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI;Seoul;20 August 2008 through22 August 2008
Available from: 2010-02-01 Created: 2010-02-01 Last updated: 2013-03-17

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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
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  • nn-NB
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  • Other locale
More languages
Output format
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  • asciidoc
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