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dc.contributor.authorRead, Jesseen_NZ
dc.contributor.authorReutemann, Peteren_NZ
dc.contributor.authorPfahringer, Bernharden_NZ
dc.contributor.authorHolmes, Geoffreyen_NZ
dc.date.accessioned2016-04-26T04:04:07Z
dc.date.available2016en_NZ
dc.date.available2016-04-26T04:04:07Z
dc.date.issued2016en_NZ
dc.identifier.citationRead, J., Reutemann, P., Pfahringer, B., & Holmes, G. (2016). MEKA: A multi-label/multi-target extension to WEKA. Journal of Machine Learning Research, 17(21), 1–5.en
dc.identifier.issn1533-7928en_NZ
dc.identifier.urihttps://hdl.handle.net/10289/10136
dc.description.abstractMulti-label classification has rapidly attracted interest in the machine learning literature, and there are now a large number and considerable variety of methods for this type of learning. We present MEKA: an open-source Java framework based on the well-known WEKA library. MEKA provides interfaces to facilitate practical application, and a wealth of multi-label classifiers, evaluation metrics, and tools for multi-label experiments and development. It supports multi-label and multi-target data, including in incremental and semi- supervised contexts.en_NZ
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.rights© 2016 Jesse Read, Peter Reutemann, Bernhard Pfahringer, and Geoff Holmes.
dc.subjectclassification
dc.subjectlearning
dc.subjectmulti-label
dc.subjectmulti-target
dc.subjectincremental
dc.subjectcomputer science
dc.titleMEKA: A multi-label/multi-target extension to WEKAen_NZ
dc.typeJournal Article
dc.relation.isPartOfJournal of Machine Learning Researchen_NZ
pubs.begin-page1
pubs.elements-id138425
pubs.end-page5
pubs.issue21en_NZ
pubs.publisher-urlhttp://jmlr.org/papers/v17/12-164.htmlen_NZ
pubs.volume17en_NZ


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