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dc.contributor.authorLeathart, Timen_NZ
dc.contributor.authorPfahringer, Bernharden_NZ
dc.contributor.authorFrank, Eibeen_NZ
dc.contributor.editorFrasconi, P.en_NZ
dc.contributor.editorLandwehr, N.en_NZ
dc.contributor.editorManco, G.en_NZ
dc.contributor.editorVreeken, J.en_NZ
dc.coverage.spatialRiva del Garda, Italyen_NZ
dc.date.accessioned2016-11-28T23:03:35Z
dc.date.available2016en_NZ
dc.date.available2016-11-28T23:03:35Z
dc.date.issued2016en_NZ
dc.identifier.citationLeathart, T., Pfahringer, B., & Frank, E. (2016). Building ensembles of adaptive nested dichotomies with random-pair selection. In P. Frasconi, N. Landwehr, G. Manco, & J. Vreeken (Eds.), Proceedings of Joint European Conference on Machine Learning and Knowledge Discovery in Databases (Vol. Part II, LNAI 9852, pp. 179–194). Cham, Switzerland: Springer. http://doi.org/10.1007/978-3-319-46227-1_12en
dc.identifier.isbn9783319462264en_NZ
dc.identifier.issn0302-9743en_NZ
dc.identifier.urihttps://hdl.handle.net/10289/10767
dc.description.abstractA system of nested dichotomies is a method of decomposing a multi-class problem into a collection of binary problems. Such a system recursively applies binary splits to divide the set of classes into two subsets, and trains a binary classifier for each split. Although ensembles of nested dichotomies with random structure have been shown to perform well in practice, using a more sophisticated class subset selection method can be used to improve classification accuracy. We investigate an approach to this problem called random-pair selection, and evaluate its effectiveness compared to other published methods of subset selection. We show that our method outperforms other methods in many cases when forming ensembles of nested dichotomies, and is at least on par in all other cases. The software related to this paper is available at https://svn.cms.waikato.ac.nz/svn/weka/trunk/packages/ internal/ensemblesOfNestedDichotomies/.en_NZ
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherSpringeren_NZ
dc.rightsThis is an author’s accepted version of an article published in the Proceedings of Joint European Conference on Machine Learning and Knowledge Discovery in Databases. © Springer International Publishing AG 2016.
dc.sourceECML PKDD 2016en_NZ
dc.subjectcomputer science
dc.subjectMachine learning
dc.subjectMachine learning
dc.titleBuilding ensembles of adaptive nested dichotomies with random-pair selectionen_NZ
dc.typeConference Contribution
dc.identifier.doi10.1007/978-3-319-46227-1_12en_NZ
dc.relation.isPartOfProceedings of Joint European Conference on Machine Learning and Knowledge Discovery in Databasesen_NZ
pubs.begin-page179
pubs.elements-id142692
pubs.end-page194
pubs.finish-date2016-09-23en_NZ
pubs.place-of-publicationCham, Switzerland
pubs.start-date2016-09-19en_NZ
pubs.volumePart II, LNAI 9852en_NZ
dc.identifier.eissn1611-3349en_NZ


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