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dc.contributor.authorHolmes, Geoffrey
dc.contributor.authorRichard, Kirkby
dc.contributor.authorPfahringer, Bernhard
dc.coverage.spatialConference held at Portugalen_NZ
dc.date.accessioned2008-11-28T00:56:14Z
dc.date.available2008-11-28T00:56:14Z
dc.date.issued2005
dc.identifier.citationHolmes, G., Richard, K., Pfahringer, B. (2005). Tie-breaking in Hoeffding trees. In proceedings of the Second International Workshop on Knowledge Discovery from Data Streams, Porto, Portugal, 2005.en_US
dc.identifier.urihttps://hdl.handle.net/10289/1488
dc.description.abstractA thorough examination of the performance of Hoeffding trees, state-of-the-art in classification for data streams, on a range of datasets reveals that tie breaking, an essential but supposedly rare procedure, is employed much more than expected. Testing with a lightweight method for handling continuous attributes, we find that the excessive invocation of tie breaking causes performance to degrade significantly on complex and noisy data. Investigating ways to reduce the number of tie breaks, we propose an adaptive method that overcomes the problem while not significantly affecting performance on simpler datasets.en_US
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherECML/PKDDen_NZ
dc.subjectcomputer scienceen_US
dc.subjectHoeffding treesen_US
dc.subjectMachine learning
dc.titleTie-breaking in Hoeffding treesen_US
dc.typeConference Contributionen_US
dc.relation.isPartOfThe Second International Workshop on Knowledge Discovery in Data Streamsen_NZ
pubs.begin-page107en_NZ
pubs.elements-id15919
pubs.end-page116en_NZ
pubs.finish-date2005-10-07en_NZ
pubs.start-date2005-10-03en_NZ
pubs.volumeProceedings of the Workshop W6 The Second International Workshop on Knowledge Discovery in Data Streamsen_NZ


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