Please use this identifier to cite or link to this item: http://hdl.handle.net/2381/39321
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dc.contributor.authorKrawczyk, Bartosz-
dc.contributor.authorMinku, Leandro L.-
dc.contributor.authorGama, Joao-
dc.contributor.authorStefanowski, Jerzy-
dc.contributor.authorWozniak, Michal-
dc.date.accessioned2017-02-02T15:35:45Z-
dc.date.issued2017-02-03-
dc.identifier.citationInformation Fusion, 2017, 37, pp. 132–156en
dc.identifier.issn1566-2535-
dc.identifier.urihttp://www.sciencedirect.com/science/article/pii/S1566253516302329en
dc.identifier.urihttp://hdl.handle.net/2381/39321-
dc.descriptionThe file associated with this record is embargoed until 18 months after the date of publication. The final published version may be available through the links above. Following the embargo period the above license applies.en
dc.description.abstractIn many applications of information systems learning algorithms have to act in dynamic environments where data are collected in the form of transient data streams. Compared to static data mining, processing streams imposes new computational requirements for algorithms to incrementally process incoming examples while using limited memory and time. Furthermore, due to the non-stationary characteristics of streaming data, prediction models are often also required to adapt to concept drifts. Out of several new proposed stream algorithms, ensembles play an important role, in particular for non-stationary environments. This paper surveys research on ensembles for data stream classification as well as regression tasks. Besides presenting a comprehensive spectrum of ensemble approaches for data streams, we also discuss advanced learning concepts such as imbalanced data streams, novelty detection, active and semi-supervised learning, complex data representations and structured outputs. The paper concludes with a discussion of open research problems and lines of future research.en
dc.language.isoenen
dc.publisherElsevieren
dc.rightsCopyright © Elsevier, 2017. This article is distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/ ), which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.en
dc.titleEnsemble learning for data stream analysis: a surveyen
dc.typeJournal Articleen
dc.identifier.doi10.1016/j.inffus.2017.02.004-
dc.description.statusPeer-revieweden
dc.description.versionPost-printen
dc.type.subtypeReview-
pubs.organisational-group/Organisationen
pubs.organisational-group/Organisation/COLLEGE OF SCIENCE AND ENGINEERINGen
pubs.organisational-group/Organisation/COLLEGE OF SCIENCE AND ENGINEERING/Department of Computer Scienceen
dc.rights.embargodate2018-08-03-
dc.dateaccepted2017-02-01-
Appears in Collections:Published Articles, Dept. of Computer Science

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