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dc.contributor.authorRead, Jesse
dc.contributor.authorPfahringer, Bernhard
dc.contributor.authorHolmes, Geoffrey
dc.contributor.authorFrank, Eibe
dc.coverage.spatialConference held at Bled, Sloveniaen_NZ
dc.identifier.citationRead, J., Pfahringer, B., Holmes, G. & Frank, E. (2009). Classifier chains for multi-label classification. In Proceedings of European conference on Machine Learning and Knowledge Discovery in Databases 2009 (ECML PKDD 2009), Part II, LNAI 5782(pp. 254-269). Berlin: Springer.en
dc.description.abstractThe widely known binary relevance method for multi-label classification, which considers each label as an independent binary problem, has been sidelined in the literature due to the perceived inadequacy of its label-independence assumption. Instead, most current methods invest considerable complexity to model interdependencies between labels. This paper shows that binary relevance-based methods have much to offer, especially in terms of scalability to large datasets. We exemplify this with a novel chaining method that can model label correlations while maintaining acceptable computational complexity. Empirical evaluation over a broad range of multi-label datasets with a variety of evaluation metrics demonstrates the competitiveness of our chaining method against related and state-of-the-art methods, both in terms of predictive performance and time complexity.en
dc.sourceJoint European Conference on Machine Learning (ECML)/European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD)en_NZ
dc.subjectcomputer scienceen
dc.subjectmulti-label classificationen
dc.subjectMachine learning
dc.subjectMachine learning
dc.titleClassifier chains for multi-label classificationen
dc.typeConference Contributionen
dc.relation.isPartOfProc European Conference on Machine Learning and Knowledge Discovery in Databases 2009 (ECML PKDD 2009), Part II, LNAI 5782en_NZ
pubs.issuePART 2en_NZ
pubs.volumeLNAI 5782en_NZ

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