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dc.contributor.authorSun, Quan
dc.contributor.authorPfahringer, Bernhard
dc.coverage.spatialConference held at Perth, Australiaen_NZ
dc.date.accessioned2012-05-25T00:59:48Z
dc.date.available2012-05-25T00:59:48Z
dc.date.copyright2011
dc.date.issued2011
dc.identifier.citationSun, Q. & Pfahringer, B. (2011). Bagging ensemble selection. In Lecture Notes in Computer Science, 2011, Volume 7106, AI 2011: Advances in Artificial Intelligence. Pp. 251-260.en_NZ
dc.identifier.urihttps://hdl.handle.net/10289/6366
dc.description.abstractEnsemble selection has recently appeared as a popular ensemble learning method, not only because its implementation is fairly straightforward, but also due to its excellent predictive performance on practical problems. The method has been highlighted in winning solutions of many data mining competitions, such as the Netix competition, the KDD Cup 2009 and 2010, the UCSD FICO contest 2010, and a number of data mining competitions on the Kaggle platform. In this paper we present a novel variant: bagging ensemble selection. Three variations of the proposed algorithm are compared to the original ensemble selection algorithm and other ensemble algorithms. Experiments with ten real world problems from diverse domains demonstrate the benefit of the bagging ensemble selection algorithm.en_NZ
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherSpringeren_NZ
dc.rightsThis is the author's accepted version. The original publication is available at www.springerlink.com. Copyright Springer-Verlag Berlin Heidelberg 2011.en_NZ
dc.source24th Australasian Joint Conference on Artificial Intelligence (AI)en_NZ
dc.titleBagging ensemble selectionen_NZ
dc.typeChapter in Booken_NZ
dc.identifier.doi10.1007/978-3-642-25832-9_26en_NZ
dc.relation.isPartOfProc Twenty-fourth Australasian Joint Conference on Advances in Artificial Intelligenceen_NZ
pubs.begin-page251en_NZ
pubs.elements-id21610
pubs.end-page260en_NZ
pubs.finish-date2011-12-08en_NZ
pubs.place-of-publicationBerlinen_NZ
pubs.start-date2011-12-05en_NZ
pubs.volumeLNAI 7106en_NZ


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