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dc.contributor.authorLeathart, Timen_NZ
dc.contributor.authorFrank, Eibeen_NZ
dc.contributor.authorPfahringer, Bernharden_NZ
dc.contributor.authorHolmes, Geoffreyen_NZ
dc.contributor.editorYang, Q.en_NZ
dc.contributor.editorZhou, Z.-H.en_NZ
dc.contributor.editorGong, Z.en_NZ
dc.contributor.editorZhang, M.-L.en_NZ
dc.contributor.editorHuang, S.-J.en_NZ
dc.coverage.spatialMacau, Chinaen_NZ
dc.date.accessioned2019-06-05T21:45:37Z
dc.date.available2019en_NZ
dc.date.available2019-06-05T21:45:37Z
dc.date.issued2019en_NZ
dc.identifier.citationLeathart, T., Frank, E., Pfahringer, B., & Holmes, G. (2019). Ensembles of nested dichotomies with multiple subset evaluation. In Q. Yang, Z.-H. Zhou, Z. Gong, M.-L. Zhang, & S.-J. Huang (Eds.), Advances in Knowledge Discovery and Data Mining: Proc 23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2019), LNCS 11439 (Vol. Part I, pp. 81–93). Cham: Springer. https://doi.org/10.1007/978-3-030-16148-4_7en
dc.identifier.isbn978-3-030-16147-7en_NZ
dc.identifier.urihttps://hdl.handle.net/10289/12597
dc.description.abstractA system of nested dichotomies (NDs) is a method of decomposing a multiclass 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. Many methods have been proposed to perform this split, each with various advantages and disadvantages. In this paper, we present a simple, general method for improving the predictive performance of NDs produced by any subset selection techniques that employ randomness to construct the subsets. We provide a theoretical expectation for performance improvements, as well as empirical results showing that our method improves the root mean squared error of NDs, regardless of whether they are employed as an individual model or in an ensemble setting.
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherSpringeren_NZ
dc.rights© 2019 Springer, Cham. This is the author's accepted version. The final publication is available at Springer via dx.doi.org/10.1007/978-3-030-16148-4_7
dc.subjectcomputer scienceen_NZ
dc.subjectmachine learningen_NZ
dc.titleEnsembles of nested dichotomies with multiple subset evaluationen_NZ
dc.typeConference Contribution
dc.identifier.doi10.1007/978-3-030-16148-4_7en_NZ
dc.relation.isPartOfAdvances in Knowledge Discovery and Data Mining: Proc 23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2019), LNCS 11439en_NZ
pubs.begin-page81
pubs.elements-id237119
pubs.end-page93
pubs.finish-date2019-04-17en_NZ
pubs.place-of-publicationChamen_NZ
pubs.publication-statusPublisheden_NZ
pubs.start-date2019-04-14en_NZ
pubs.volumePart Ien_NZ


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