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Scalable and efficient multi-label classification for evolving data streams

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dc.contributor.author Read, Jesse
dc.contributor.author Bifet, Albert
dc.contributor.author Holmes, Geoffrey
dc.contributor.author Pfahringer, Bernhard
dc.date.accessioned 2012-09-17T03:09:59Z
dc.date.available 2012-09-17T03:09:59Z
dc.date.copyright 2012-07-21
dc.date.issued 2012
dc.identifier.citation Read, J., Bifet, A., Holmes, G. and Pfahringer, B. (2012). Scalable and efficient multi-label classification for evolving data streams. Machine Learning, 88(1-2), 243-272. en_NZ
dc.identifier.issn 1573-0565
dc.identifier.issn 0885-6125
dc.identifier.uri http://hdl.handle.net/10289/6633
dc.description.abstract Many challenging real world problems involve multi-label data streams. Efficient methods exist for multi-label classification in non-streaming scenarios. However, learning in evolving streaming scenarios is more challenging, as classifiers must be able to deal with huge numbers of examples and to adapt to change using limited time and memory while being ready to predict at any point. This paper proposes a new experimental framework for learning and evaluating on multi-label data streams, and uses it to study the performance of various methods. From this study, we develop a multi-label Hoeffding tree with multi-label classifiers at the leaves. We show empirically that this method is well suited to this challenging task. Using our new framework, which allows us to generate realistic multi-label data streams with concept drift (as well as real data), we compare with a selection of baseline methods, as well as new learning methods from the literature, and show that our Hoeffding tree method achieves fast and more accurate performance. en_NZ
dc.language.iso en
dc.publisher Springer en_NZ
dc.relation.ispartof Machine Learning
dc.title Scalable and efficient multi-label classification for evolving data streams en_NZ
dc.type Journal Article en_NZ
dc.identifier.doi 10.1007/s10994-012-5279-6 en_NZ


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