Active feature acquisition for opinion stream classification under drift

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dc.identifier.uri http://dx.doi.org/10.15488/9818
dc.identifier.uri https://www.repo.uni-hannover.de/handle/123456789/9875
dc.contributor.author Shivakumaraswamy, Ranjith
dc.contributor.author Beyer, Christian
dc.contributor.author Unnikrishnan, Vishnu
dc.contributor.author Ntoutsi, Eirini
dc.contributor.author Spiliopoulou, Myra
dc.date.accessioned 2020-05-18T09:16:27Z
dc.date.available 2020-05-18T09:16:27Z
dc.date.issued 2019
dc.identifier.citation Shivakumaraswamy, R.; Beyer, C.; Unnikrishnan, V.; Ntoutsi, Eirini; Spiliopoulou, M.: Active feature acquisition for opinion stream classification under drift. In: CEUR Workshop Proceedings 2444 (2019), S. 108-111.
dc.description.abstract Active stream learning is frequently used to acquire labels for instances and less frequently to determine which features should be considered as the stream evolves. We introduce a framework for active feature selection, intended to adapt the feature space of a polarity learner over a stream of opinionated documents. We report on the first results of our framework on substreams of reviews on different product categories. eng
dc.language.iso eng
dc.publisher Aachen : RWTH
dc.relation.ispartofseries CEUR Workshop Proceedings 2444 (2019)
dc.rights CC BY 4.0 Unported
dc.rights.uri https://creativecommons.org/licenses/by/4.0/
dc.subject Active Feature Acquisition eng
dc.subject Opinion Stream Classification eng
dc.subject Feature acquisition eng
dc.subject Feature space eng
dc.subject Product categories eng
dc.subject Stream classification eng
dc.subject Sub-streams eng
dc.subject Classification (of information) eng
dc.subject.ddc 004 | Informatik ger
dc.title Active feature acquisition for opinion stream classification under drift eng
dc.type article
dc.type Text
dc.relation.issn 1613-0073
dc.bibliographicCitation.volume 2444
dc.bibliographicCitation.firstPage 108
dc.bibliographicCitation.lastPage 111
dc.description.version publishedVersion
tib.accessRights frei zug�nglich


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