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dc.contributor.authorvan Rijn, Jan N.
dc.contributor.authorHolmes, Geoffrey
dc.contributor.authorPfahringer, Bernhard
dc.contributor.authorVanschoren, Joaquin
dc.contributor.editorDžeroski, Saso
dc.contributor.editorPanov, Panov
dc.contributor.editorKocev, Dragi
dc.contributor.editorTodorovski, Ljupco
dc.coverage.spatialBled Slovenia
dc.date.accessioned2015-05-25T02:16:01Z
dc.date.available2014
dc.date.available2015-05-25T02:16:01Z
dc.date.issued2014
dc.identifier.citationvan Rijn, J. N., Holmes, G., Pfahringer, B., & Vanschoren, J. (2014). Algorithm selection on data streams. In S. Džeroski, P. Panov, D. Kocev, & L. Todorovski (Eds.), Proceedings of 17th International Conference on Discovery Science (Vol. LNAI 8777, pp. 325–336). Springer International Publishing. http://doi.org/10.1007/978-3-319-11812-3_28en
dc.identifier.isbn9783319118116
dc.identifier.issn0302-9743
dc.identifier.urihttps://hdl.handle.net/10289/9337
dc.description.abstractWe explore the possibilities of meta-learning on data streams, in particular algorithm selection. In a first experiment we calculate the characteristics of a small sample of a data stream, and try to predict which classifier performs best on the entire stream. This yields promising results and interesting patterns. In a second experiment, we build a meta-classifier that predicts, based on measurable data characteristics in a window of the data stream, the best classifier for the next window. The results show that this meta-algorithm is very competitive with state of the art ensembles, such as OzaBag, OzaBoost and Leveraged Bagging. The results of all experiments are made publicly available in an online experiment database, for the purpose of verifiability, reproducibility and generalizability.
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherSpringer International Publishing
dc.relation.urihttp://dx.doi.org/10.1007/978-3-319-11812-3_28
dc.sourceDS 2014
dc.subjectData Stream Mining
dc.subjectMeta Learning
dc.subjectMachine learning
dc.titleAlgorithm selection on data streams
dc.typeConference Contribution
dc.identifier.doi10.1007/978-3-319-11812-3_28
dc.relation.isPartOfProceedings of 17th International Conference on Discovery Science
pubs.begin-page325
pubs.elements-id117416
pubs.end-page336
pubs.finish-date2014-10-10
pubs.start-date2014-10-08
pubs.volumeLNAI 8777
dc.identifier.eissn1611-3349


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