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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.ORCID-id: 0000-0003-0274-9026
Högskolan i Borås, Institutionen Handels- och IT-högskolan.ORCID-id: 0000-0003-0412-6199
2008 (engelsk)Konferansepaper, Publicerat paper (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
2008.
Emneord [en]
ensembles, diversity, Computer Science, Machine Learning, Data Mining
Emneord [sv]
data mining
HSV kategori
Identifikatorer
URN: urn:nbn:se:hj:diva-45815Lokal ID: 0;0;miljJAILISBN: 978-0-7695-3495-4 (tryckt)OAI: oai:DiVA.org:hj-45815DiVA, id: diva2:1348929
Konferanse
Seventh International Conference on Machine Learning and Applications
Merknad

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.

Tilgjengelig fra: 2019-09-06 Laget: 2019-09-06 Sist oppdatert: 2019-09-06bibliografisk kontrollert

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Löfström, TuveJohansson, Ulf

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