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On the Use of Accuracy and Diversity Measures for Evaluating and Selecting Ensembles of Classifiers
Högskolan i Borås, Institutionen Handels- och IT-högskolan.
Högskolan i Borås, Institutionen Handels- och IT-högskolan.
University of Skövde, Sweden.
2008 (English)In: Proceedings - 7th International Conference on Machine Learning and Applications, ICMLA 2008, IEEE, 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
IEEE, 2008. p. 127-132
Keywords [en]
ensembles, diversity, Computer Science, Machine Learning, Data Mining
Keywords [sv]
data mining
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:kth:diva-221591DOI: 10.1109/ICMLA.2008.102ISI: 000263205800018Scopus ID: 2-s2.0-60649083359Local ID: 2320/4411ISBN: 978-0-7695-3495-4 (print)OAI: oai:DiVA.org:kth-221591DiVA, id: diva2:1175211
Conference
Seventh International Conference on Machine Learning and Applications
Note

Sponsorship:

This work was supported by the Information Fusion Research Program (www.infofusion.se) at the University of Skövde, Sweden, in partnership with the Swedish Knowledge Foundation under grant 2003/0104.

QC 20180206

Available from: 2018-01-17 Created: 2018-01-17 Last updated: 2018-02-06Bibliographically approved

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