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dc.contributor.authorBifet, Albert
dc.contributor.authorHolmes, Geoffrey
dc.contributor.authorPfahringer, Bernhard
dc.contributor.authorFrank, Eibe
dc.coverage.spatialConference held at Hyderabad, Indiaen_NZ
dc.date.accessioned2010-06-13T21:56:38Z
dc.date.available2010-06-13T21:56:38Z
dc.date.issued2010
dc.identifier.citationBifet, A., Holmes, G., Pfahringer, B. & Frank, E. (2010). Fast perceptron decision tree learning from evolving data streams. In M.J. Zaki, J. X. Yu, B. Ravindran & V. Pudi (Eds.), Advances in Knowledge Discovery and Data Mining: 14th Pacific-Asia Conference, PAKDD 2010, Hyderabad, India, June 21-24, 2010. Proceedings. Part II (pp. 299-310). Berlin, Germany: Springer.en_NZ
dc.identifier.urihttps://hdl.handle.net/10289/3983
dc.description.abstractMining of data streams must balance three evaluation dimensions: accuracy, time and memory. Excellent accuracy on data streams has been obtained with Naive Bayes Hoeffding Trees—Hoeffding Trees with naive Bayes models at the leaf nodes—albeit with increased runtime compared to standard Hoeffding Trees. In this paper, we show that runtime can be reduced by replacing naive Bayes with perceptron classifiers, while maintaining highly competitive accuracy. We also show that accuracy can be increased even further by combining majority vote, naive Bayes, and perceptrons. We evaluate four perceptron-based learning strategies and compare them against appropriate baselines: simple perceptrons, Perceptron Hoeffding Trees, hybrid Naive Bayes Perceptron Trees, and bagged versions thereof. We implement a perceptron that uses the sigmoid activation function instead of the threshold activation function and optimizes the squared error, with one perceptron per class value. We test our methods by performing an evaluation study on synthetic and real-world datasets comprising up to ten million examples.en_NZ
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherSpringer Berlinen_NZ
dc.relation.urihttp://www.springerlink.com/content/212q90037v867278/en_NZ
dc.rightsThis is an author’s accepted version of a paper published in Advances in Knowledge Discovery and Data Mining: 14th Pacific-Asia Conference, PAKDD 2010, Hyderabad, India, June 21-24, 2010. Proceedings. Part II.en_NZ
dc.sourcePAKDD 2010en_NZ
dc.subjectcomputer scienceen_NZ
dc.subjectdata miningen_NZ
dc.subjectdata streamen_NZ
dc.subjectdecision treeen_NZ
dc.subjectHoeffding treeen_NZ
dc.subjectMachine learning
dc.subjectMachine learning
dc.titleFast perceptron decision tree learning from evolving data streamsen_NZ
dc.typeConference Contributionen_NZ
dc.identifier.doi10.1007/978-3-642-13672-6_30en_NZ
dc.relation.isPartOfProc 14th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Miningen_NZ
pubs.begin-page299en_NZ
pubs.elements-id19555
pubs.end-page310en_NZ
pubs.finish-date2010-06-24en_NZ
pubs.issuePART 2en_NZ
pubs.place-of-publicationGermanyen_NZ
pubs.start-date2010-06-21en_NZ
pubs.volumeLNCS 6119en_NZ


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