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dc.contributor.authorFaithfull, William J. .
dc.contributor.authorRodríguez Diez, Juan José 
dc.contributor.authorKuncheva, Ludmila I. .
dc.date.accessioned2018-03-09T08:42:32Z
dc.date.issued2019-01
dc.identifier.issn1566-2535
dc.identifier.urihttp://hdl.handle.net/10259/4749
dc.description.abstractDetecting change in multivariate data is a challenging problem, especially when class labels are not available. There is a large body of research on univariate change detection, notably in control charts developed originally for engineering applications. We evaluate univariate change detection approaches —including those in the MOA framework — built into ensembles where each member observes a feature in the input space of an unsupervised change detection problem. We present a comparison between the ensemble combinations and three established ‘pure’ multivariate approaches over 96 data sets, and a case study on the KDD Cup 1999 network intrusion detection dataset. We found that ensemble combination of univariate methods consistently outperformed multivariate methods on the four experimental metrics.en
dc.description.sponsorshipproject RPG-2015-188 funded by The Leverhulme Trust, UK; Spanish Ministry of Economy and Competitiveness through project TIN 2015-67534-P and the Spanish Ministry of Education, Culture and Sport through Mobility Grant PRX16/00495. The 96 datasets were originally curated for use in the work of Fernández-Delgado et al. [53] and accessed from the personal web page of the author5. The KDD Cup 1999 dataset used in the case study was accessed from the UCI Machine Learning Repository [10]en
dc.format.mimetypeapplication/pdf
dc.language.isoenges
dc.publisherElsevieren
dc.relation.ispartofInformation Fusion. 2019, V. 45, p. 202-214en
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.titleCombining univariate approaches for ensemble change detection in multivariate dataen
dc.typeArtículoes
dc.typeinfo:eu-repo/semantics/article
dc.date.embargo2021-01
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess
dc.relation.publisherversionhttps://doi.org/10.1016/j.inffus.2018.02.003
dc.identifier.doi10.1016/j.inffus.2018.02.003
dc.relation.projectIDinfo:eu-repo/grantAgreement/Leverhulme Trust/RPG-2015-188
dc.relation.projectIDinfo:eu-repo/grantAgreement/MINECO/TIN 2015-67534-P
dc.type.hasVersioninfo:eu-repo/semantics/acceptedVersionen


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