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MOA: Massive Online Analysis

dc.contributor.authorBifet, Albert
dc.contributor.authorHolmes, Geoffrey
dc.contributor.authorKirkby, Richard Brendon
dc.contributor.authorPfahringer, Bernhard
dc.date.accessioned2010-06-13T22:08:53Z
dc.date.available2010-06-13T22:08:53Z
dc.date.issued2010
dc.identifier.citationBifet, A., Holmes, G., Kirkby, R., Pfahringer, B. (2010). MOA: Massive Online Analysis. Journal of Machine Learning Research, 11, 1601-1604.en_NZ
dc.identifier.issn1533-7928
dc.identifier.urihttps://hdl.handle.net/10289/3984
dc.description.abstractMassive Online Analysis (MOA) is a software environment for implementing algorithms and running experiments for online learning from evolving data streams. MOA includes a collection of offline and online methods as well as tools for evaluation. In particular, it implements boosting, bagging, and Hoeffding Trees, all with and without Naïve Bayes classifiers at the leaves. MOA supports bi-directional interaction with WEKA, the Waikato Environment for Knowledge Analysis, and is released under the GNU GPL license.en_NZ
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherMassachusetts Institute of Technology Pressen_NZ
dc.relation.urihttp://jmlr.csail.mit.edu/papers/v11/bifet10a.htmlen_NZ
dc.rightsThis article has been published in the Journal of Machine Learning Research. Copyright 2010 Albert Bifet, Geoff Holmes, Richard Kirkby and Bernhard Pfahringer.
dc.subjectcomputer scienceen_NZ
dc.subjectMassive Online Analysisen_NZ
dc.subjectMOAen_NZ
dc.subjectWEKAen_NZ
dc.subjectdata miningen_NZ
dc.subjectMachine learning
dc.titleMOA: Massive Online Analysisen_NZ
dc.typeJournal Articleen_NZ
dc.relation.isPartOfJournal of Machine Learning Researchen_NZ
pubs.begin-page1601en_NZ
pubs.editionMayen_NZ
pubs.elements-id34967
pubs.end-page1604en_NZ
pubs.volume11en_NZ


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