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dc.contributor.authorRead, Jesseen_NZ
dc.contributor.authorPfahringer, Bernharden_NZ
dc.contributor.authorHolmes, Geoffreyen_NZ
dc.contributor.authorFrank, Eibeen_NZ
dc.date.accessioned2021-03-25T22:32:44Z
dc.date.available2021-03-25T22:32:44Z
dc.date.issued2021en_NZ
dc.identifier.citationRead, J, Pfahringer, B., Holmes, G., & Frank, E. (2021). Classifier chains: A review and perspectives. Journal of Artificial Intelligence Research, 70, 683–718. https://doi.org/10.1613/jair.1.12376en
dc.identifier.urihttps://hdl.handle.net/10289/14201
dc.description.abstractThe family of methods collectively known as classifier chains has become a popular approach to multi-label learning problems. This approach involves chaining together off-the-shelf binary classifiers in a directed structure, such that individual label predictions become features for other classifiers. Such methods have proved flexible and effective and have obtained state-of-the-art empirical performance across many datasets and multi-label evaluation metrics. This performance led to further studies of the underlying mechanism and efficacy, and investigation into how it could be improved. In the recent decade, numerous studies have explored the theoretical underpinnings of classifier chains, and many improvements have been made to the training and inference procedures, such that this method remains among the best options for multi-label learning. Given this past and ongoing interest, which covers a broad range of applications and research themes, the goal of this work is to provide a review of classifier chains, a survey of the techniques and extensions provided in the literature, as well as perspectives for this approach in the domain of multi-label classification in the future. We conclude positively, with a number of recommendations for researchers and practitioners, as well as outlining key issues for future research.en_NZ
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherAI Access Foundationen_NZ
dc.rights© 2021 AI Access Foundation. All rights reserved.
dc.subjectcomputer scienceen_NZ
dc.subjectMachine learning
dc.titleClassifier chains: A review and perspectivesen_NZ
dc.typeJournal Article
dc.identifier.doi10.1613/jair.1.12376en_NZ
dc.relation.isPartOfJournal of Artificial Intelligence Researchen_NZ
pubs.begin-page683
pubs.elements-id259592
pubs.end-page718
pubs.publication-statusPublished onlineen_NZ
pubs.volume70en_NZ
dc.identifier.eissn1076-9757en_NZ


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