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dc.contributor.advisor
dc.contributor.authorL. Lobo, Jesus
dc.contributor.authorDel Ser, Javier
dc.contributor.authorBilbao, Miren Nekane
dc.contributor.authorLaña, Ibai
dc.contributor.authorSalcedo-Sanz, Sancho
dc.date.accessioned2016-12-05T08:58:41Z
dc.date.available2016-12-05T08:58:41Z
dc.date.issued2017
dc.identifier.citationIntelligent Distributed Computing X, Volume 678 of the series Studies in Computational Intelligence, pp 237-246en
dc.identifier.isbn978-3-319-48828-8en
dc.identifier.issn1860-949Xen
dc.identifier.urihttp://hdl.handle.net/11556/352
dc.description.abstractIn the last decade the interest in adaptive models for non-stationary environments has gained momentum within the research community due to an increasing number of application scenarios generating non-stationary data streams. In this context the literature has been specially rich in terms of ensemble techniques, which in their majority have focused on taking advantage of past information in the form of already trained predictive models and other alternatives alike. This manuscript elaborates on a rather different approach, which hinges on extracting the essential predictive information of past trained models and determining therefrom the best candidates (intelligent sample matchmaking) for training the predictive model of the current data batch. This novel perspective is of inherent utility for data streams characterized by short-length unbalanced data batches, situation where the so-called trade-off between plasticity and stability must be carefully met. The approach is evaluated on a synthetic data set that simulates a non-stationary environment with recurrently changing concept drift. The proposed approach is shown to perform competitively when adapting to a sudden and recurrent change with respect to the state of the art, but without storing all the past trained models and by lessening its computational complexity in terms of model evaluations. These promising results motivate future research aimed at validating the proposed strategy on other scenarios under concept drift, such as those characterized by semi-supervised data streams.en
dc.description.sponsorshipBasque Government under its ELKARTEK research program (ref: KK-2015/0000080, BID3A project).en
dc.language.isoengen
dc.publisherSpringer International Publishingen
dc.titleA Probabilistic Sample Matchmaking Strategy for Imbalanced Data Streams with Concept Driften
dc.typeconferenceObjecten
dc.identifier.doi10.1007/978-3-319-48829-5_23en
dc.isiYesen
dc.rights.accessRightsembargoedAccessen
dc.subject.keywordsConcept Driften
dc.subject.keywordsAdaptive Learningen
dc.subject.keywordsImbalanced dataen
dc.journal.titleStudies in Computational Intelligenceen
dc.page.final246en
dc.page.initial237en
dc.volume.number678en
dc.identifier.esbn978-3-319-48829-5en


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