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dc.contributor.authorHolmes, Geoffrey
dc.contributor.authorPfahringer, Bernhard
dc.contributor.authorKirkby, Richard Brendon
dc.date.accessioned2008-10-08T23:00:52Z
dc.date.available2008-10-08T23:00:52Z
dc.date.issued2003-09
dc.identifier.citationHolmes, G., Pfahringer, B. & Kirkby, R. (2003). Mining data streams using option trees. (Working paper 08/03). Hamilton, New Zealand: University of Waikato, Department of Computer Science.en_US
dc.identifier.isbn1170-487X
dc.identifier.urihttps://hdl.handle.net/10289/1004
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 classification 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.publisherUniversity of Waikato, Department of Computer Scienceen_US
dc.relation.ispartofseriesComputer Science Working Papers
dc.subjectcomputer scienceen_US
dc.subjectclassificationen_US
dc.subjectoption treesen_US
dc.subjectensemble methodsen_US
dc.subjectdata streamsen_US
dc.titleMining data streams using option treesen_US
dc.typeWorking Paperen_US
uow.relation.series08/03


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