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dc.contributor.authorHolmes, Geoffreyen_US
dc.contributor.authorKirkby, Richard Brendonen_US
dc.contributor.authorPfahringer, Bernharden_US
dc.date.accessioned2008-03-19T04:58:04Z
dc.date.available2007-07-24en_US
dc.date.available2008-03-19T04:58:04Z
dc.date.issued2004-01-01en_US
dc.identifier.citationHolmes, G., Kirkby, R.,& Pfahringer, B. (2004). Mining Data Streams Using Option Trees. Rev. ed. (Working paper series. University of Waikato, Department of Computer Science. No. 03/2004). Hamilton, New Zealand: University of Waikato.en_US
dc.identifier.urihttps://hdl.handle.net/10289/93
dc.description.abstractThe data stream model for data mining places harsh restrictions on a learning algorithm. A model must be induced following the briefest interrogation of the data, must use only available memory and must update itself over time within these constraints. Additionally, the model must be able to be used for data mining at any point in time. This paper describes a data stream classi_cation algorithm using an ensemble of option trees. The ensemble of trees is induced by boosting and iteratively combined into a single interpretable model. The algorithm is evaluated using benchmark datasets for accuracy against state-of-the-art algorithms that make use of the entire dataset.en_US
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherDepartment of Computer Science, The University of Waikato
dc.relation.ispartofseriesComputer Science Working Papers
dc.subjectMachine learning
dc.titleMining data streams using option trees (revised edition, 2004)en_US
dc.typeWorking Paperen_US
uow.relation.series03/2004
pubs.elements-id52825
pubs.place-of-publicationNew Zealanden_NZ


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