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dc.contributor.authorRead, Jesse
dc.contributor.authorPfahringer, Bernhard
dc.contributor.authorHolmes, Geoffrey
dc.coverage.spatialConference held at Pisa, Italyen_NZ
dc.date.accessioned2013-10-16T01:53:04Z
dc.date.available2013-10-16T01:53:04Z
dc.date.copyright2008-12
dc.date.issued2008
dc.identifier.citationRead, J., Pfahringer, B. & Holmes, G. (2008). Multi-label classification using ensembles of pruned sets. In Proceedings of the 8th IEEE International Conference on Data Mining, 15-19 December, 2008 (pp. 995-1000). Washington, DC, USA: IEEE.en_NZ
dc.identifier.urihttps://hdl.handle.net/10289/8077
dc.description.abstractThis paper presents a Pruned Sets method (PS) for multi-label classification. It is centred on the concept of treating sets of labels as single labels. This allows the classification process to inherently take into account correlations between labels. By pruning these sets, PS focuses only on the most important correlations, which reduces complexity and improves accuracy. By combining pruned sets in an ensemble scheme (EPS), new label sets can be formed to adapt to irregular or complex data. The results from experimental evaluation on a variety of multi-label datasets show that [E]PS can achieve better performance and train much faster than other multi-label methods.en_NZ
dc.format.mimetypeapplication/pdf
dc.language.isoenen_NZ
dc.publisherIEEEen_NZ
dc.relation.ispartof2008 Eighth IEEE International Conference on Data Mining
dc.relation.urihttp://www.computer.org/csdl/proceedings/icdm/2008/3502/00/3502a995-abs.htmlen_NZ
dc.rights©2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.en_NZ
dc.source8th IEEE International Conference on Data Miningen_NZ
dc.subjectcomputer scienceen_NZ
dc.subjectmulti-label classificationen_NZ
dc.subjectproblem transformationen_NZ
dc.subjectMachine learning
dc.titleMulti-label classification using ensembles of pruned setsen_NZ
dc.typeConference Contributionen_NZ
dc.identifier.doi10.1109/ICDM.2008.74en_NZ
dc.relation.isPartOfProceedings of the Eighth IEEE International Conference on Data Mining (ICDM 2008)en_NZ
pubs.begin-page995en_NZ
pubs.elements-id18338
pubs.end-page1000en_NZ
pubs.finish-date2008-12-19en_NZ
pubs.place-of-publicationLos Alamitos, Californiaen_NZ
pubs.start-date2008-12-15en_NZ


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