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dc.contributor.authorGunn, Iain A.D. .
dc.contributor.authorArnaiz González, Álvar 
dc.contributor.authorKuncheva, Ludmila I. .
dc.date.accessioned2018-03-20T10:57:20Z
dc.date.issued2018-04
dc.identifier.issn0925-2312
dc.identifier.urihttp://hdl.handle.net/10259/4757
dc.description.abstractLarge numbers of data streams are today generated in many fields. A key challenge when learning from such streams is the problem of concept drift. Many methods, including many prototype methods, have been proposed in recent years to address this problem. This paper presents a refined taxonomy of instance selection and generation methods for the classification of data streams subject to concept drift. The taxonomy allows discrimination among a large number of methods which pre-existing taxonomies for offline instance selection methods did not distinguish. This makes possible a valuable new perspective on experimental results, and provides a framework for discussion of the concepts behind different algorithm-design approaches. We review a selection of modern algorithms for the purpose of illustrating the distinctions made by the taxonomy. We present the results of a numerical experiment which examined the performance of a number of representative methods on both synthetic and real-world data sets with and without concept drift, and discuss the implications for the directions of future research in light of the taxonomy. On the basis of the experimental results, we are able to give recommendations for the experimental evaluation of algorithms which may be proposed in the future.en
dc.description.sponsorshipproject RPG-2015-188 funded by The Leverhulme Trust, UK, and TIN 2015-67534-P from the Spanish Ministry of Economy and Competitiveness. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 731593.en
dc.format.mimetypeapplication/pdf
dc.language.isoenges
dc.publisherElsevieren
dc.relation.ispartofNeurocomputing. 2012, V. 286, p. 167-178en
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectMachine learningen
dc.subjectStream classificationen
dc.subjectInstance selectionen
dc.subjectPrototype generationen
dc.subjectConcept driften
dc.titleA taxonomic look at instance-based stream classifiersen
dc.typeArtículoes
dc.typeinfo:eu-repo/semantics/article
dc.date.embargo2020-04
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess
dc.relation.publisherversionhttps://doi.org/10.1016/j.neucom.2018.01.062
dc.identifier.doi10.1016/j.neucom.2018.01.062
dc.relation.projectIDinfo:eu-repo/grantAgreement/MINECO/TIN 2015-67534-P
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/731593
dc.type.hasVersioninfo:eu-repo/semantics/acceptedVersionen


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