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On the Use of Accuracy and Diversity Measures for Evaluating and Selecting Ensembles of Classifiers
University of Borås, Sweden.ORCID iD: 0000-0003-0274-9026
University of Borås, Sweden.
University of Skövde, Sweden.
2008 (English)In: 2008 Seventh International Conference on Machine Learning and Applications, 2008, p. 127-132Conference paper, Published 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
2008. p. 127-132
National Category
Information Systems
Research subject
Computer and Systems Sciences
Identifiers
URN: urn:nbn:se:kth:diva-221592DOI: 10.1109/ICMLA.2008.102ISI: 000263205800018Scopus ID: 2-s2.0-60649083359ISBN: 978-0-7695-3495-4 (print)OAI: oai:DiVA.org:kth-221592DiVA, id: diva2:1175207
Conference
Seventh International Conference on Machine Learning and Applications (ICMLA), San Diego, CA, 11-13 Dec. 2008
Note

QC 20180209

Available from: 2018-01-17 Created: 2018-01-17 Last updated: 2018-02-09Bibliographically approved

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Löfström, TuveBoström, Henrik
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CiteExportLink to record
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