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dc.contributor.authorPetrides, George
dc.contributor.authorVerbeke, Wouter
dc.date.accessioned2022-04-04T08:07:05Z
dc.date.available2022-04-04T08:07:05Z
dc.date.created2021-10-06T22:46:21Z
dc.date.issued2022
dc.identifier.issn1384-5810
dc.identifier.urihttps://hdl.handle.net/11250/2989430
dc.description.abstractOver the years, a plethora of cost-sensitive methods have been proposed for learning on data when different types of misclassification errors incur different costs. Our contribution is a unifying framework that provides a comprehensive and insightful overview on cost-sensitive ensemble methods, pinpointing their differences and similarities via a fine-grained categorization. Our framework contains natural extensions and generalisations of ideas across methods, be it AdaBoost, Bagging or Random Forest, and as a result not only yields all methods known to date but also some not previously considered.en_US
dc.language.isoengen_US
dc.publisherSpringeren_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.titleCost-sensitive ensemble learning: a unifying frameworken_US
dc.typeJournal articleen_US
dc.typePeer revieweden_US
dc.description.versionpublishedVersionen_US
dc.rights.holderCopyright 2021 The Author(s)en_US
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode2
dc.identifier.doi10.1007/s10618-021-00790-4
dc.identifier.cristin1943965
dc.source.journalData mining and knowledge discoveryen_US
dc.source.pagenumber1-28en_US
dc.identifier.citationData mining and knowledge discovery. 2022, 36, 1-28.en_US
dc.source.volume36en_US


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