Deferral classification of evolving temporal dependent data streams

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dc.contributor.authorMayo, Michaelen_NZ
dc.contributor.authorBifet, Alberten_NZ
dc.coverage.spatialPisa, Italyen_NZ
dc.date.accessioned2016-07-20T01:48:18Z
dc.date.available2016en_NZ
dc.date.available2016-07-20T01:48:18Z
dc.date.issued2016en_NZ
dc.description.abstractData streams generated in real-time can be strongly temporally dependent. In this case, standard techniques where we suppose that class labels are not correlated may produce sub-optimal performance because the assumption is incorrect. To deal with this problem, we present in this paper a new algorithm to classify temporally correlated data based on deferral learning. This approach is suitable for learning over time-varying streams. We show how simple classifiers such as Naive Bayes can boost their performance using this new meta-learning methodology. We give an empirical validation of our new algorithm over several real and artificial datasets.en_NZ
dc.format.mimetypeapplication/pdf
dc.identifier.citationMayo, M., & Bifet, A. (2016). Deferral classification of evolving temporal dependent data streams. In Proceedings of the 31st Annual ACM Symposium on Applied Computing, New York, NY, USA, April 4-8, 2016 (pp. 952–954). New York, NY, USA: ACM. http://doi.org/10.1145/2851613.2851890en
dc.identifier.doi10.1145/2851613.2851890en_NZ
dc.identifier.isbn9781450337397en_NZ
dc.identifier.urihttps://hdl.handle.net/10289/10549
dc.language.isoen
dc.publisherACMen_NZ
dc.relation.isPartOfProceedings of the 31st Annual ACM Symposium on Applied Computingen_NZ
dc.rightsThis is an author’s accepted version of an article published in the Proceedings of the 31st Annual ACM Symposium on Applied Computing. © 2016 copyright with the authors.
dc.sourceSAC '16en_NZ
dc.subjectcomputer scienceen_NZ
dc.subjectclassificationen_NZ
dc.subjectdata streamsen_NZ
dc.subjecttemporal dependenceen_NZ
dc.subjectMachine learning
dc.titleDeferral classification of evolving temporal dependent data streamsen_NZ
dc.typeConference Contribution
pubs.begin-page952
pubs.elements-id139643
pubs.end-page954
pubs.finish-date2016-04-08en_NZ
pubs.organisational-group/Waikato
pubs.organisational-group/Waikato/FCMS
pubs.organisational-group/Waikato/FCMS/Computer Science
pubs.organisational-group/Waikato/FCMS/Computer Science/ML Group
pubs.place-of-publicationNew York, NY, USA
pubs.start-date2016-04-04en_NZ
pubs.volume04-08-April-2016en_NZ
uow.verification.statusverified
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