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dc.contributor.authorvan Rijn, Jan N.
dc.contributor.authorHolmes, Geoffrey
dc.contributor.authorPfahringer, Bernhard
dc.contributor.authorVanschoren, Joaquin
dc.coverage.spatialPrague, Czech Republic
dc.date.accessioned2015-04-29T01:26:33Z
dc.date.available2014
dc.date.available2015-04-29T01:26:33Z
dc.date.issued2014
dc.identifier.citationvan Rijn, J. N., Holmes, G., Pfahringer, B., & Vanschoren, J. (2014). Towards Meta-learning over Data Streams. In Proc International Workshop on Meta-learning and Algorithm Selection (Vol. Vol-1201, pp. 37–38). CEUR Workshop Proceedings: CEUR-WS.en
dc.identifier.issn1613-0073
dc.identifier.urihttps://hdl.handle.net/10289/9302
dc.description.abstractModern society produces vast streams of data. Many stream mining algorithms have been developed to capture general trends in these streams, and make predictions for future observations, but relatively little is known about which algorithms perform particularly well on which kinds of data. Moreover, it is possible that the characteristics of the data change over time, and thus that a different algorithm should be recommended at various points in time. Figure 1 illustrates this. As such, we are dealing with the Algorithm Selection Problem [9] in a data stream setting. Based on measurable meta-features from a window of observations from a data stream, a meta-algorithm is built that predicts the best classifier for the next window. Our results show that this meta-algorithm is competitive with state-of-the art data streaming ensembles, such as OzaBag [6], OzaBoost [6] and Leveraged Bagging [3].
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherCEUR-WS
dc.relation.urihttp://ceur-ws.org/Vol-1201/
dc.rights© 2014 Copyright for individual papers with the authors
dc.sourceMetaSel 2014
dc.subjectMachine learning
dc.titleTowards Meta-learning over Data Streams
dc.typeConference Contribution
dc.relation.isPartOfProc International Workshop on Meta-learning and Algorithm Selection
pubs.begin-page37
pubs.elements-id116981
pubs.end-page38
pubs.finish-date2014-08-19
pubs.place-of-publicationCEUR Workshop Proceedings
pubs.start-date2014-08-19
pubs.volumeVol-1201


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