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dc.contributor.authorHolmes, Geoffrey
dc.contributor.authorKirkby, Richard Brendon
dc.contributor.authorBainbridge, David
dc.date.accessioned2009-01-08T01:41:38Z
dc.date.available2009-01-08T01:41:38Z
dc.date.issued2004
dc.identifier.citationHolmes, G., Kirkby, R. & Bainbridge, D.(2004). Batch-Incremental Learning for Mining Data Streams. Working papers, University of Waikato, Department of Computer science 2004, Hamilton, New Zealand: University of Waikato.en
dc.identifier.urihttps://hdl.handle.net/10289/1749
dc.description.abstractThe data stream model for data mining places harsh restrictions on a learning algorithm. First, a model must be induced incrementally. Second, processing time for instances must keep up with their speed of arrival. Third, a model may only use a constant amount of memory, and must be ready for prediction at any point in time. We attempt to overcome these restrictions by presenting a data stream classification algorithm where the data is split into a stream of disjoint batches. Single batches of data can be processed one after the other by any standard non-incremental learning algorithm. Our approach uses ensembles of decision trees. These tree ensembles are iteratively merged into a single interpretable model of constant maximal size. Using benchmark datasets the algorithm is evaluated for accuracy against state-of-the-art algorithms that make use of the entire dataset.en
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherUniversity of Waikatoen_NZ
dc.subjectcomputer scienceen
dc.subjectclassification option treesen
dc.subjectensemble methodsen
dc.subjectdata streamsen
dc.titleBatch-Incremental Learning for Mining Data Streamsen
dc.typeWorking Paperen
uow.relation.seriesDepartment of Computer Science Working Papersen_NZ
pubs.elements-id53736


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