A novel online stacked ensemble for multi-label stream classification

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dc.citation.epage1072en_US
dc.citation.spage1063en_US
dc.contributor.authorBüyükçakır, Alicanen_US
dc.contributor.authorBonab, H.en_US
dc.contributor.authorCan, Fazlıen_US
dc.coverage.spatialTorino, Italyen_US
dc.date.accessioned2019-02-21T16:06:49Z
dc.date.available2019-02-21T16:06:49Z
dc.date.issued2018en_US
dc.departmentDepartment of Computer Engineeringen_US
dc.description.abstractAs data streams become more prevalent, the necessity for online algorithms that mine this transient and dynamic data becomes clearer. Multi-label data stream classification is a supervised learning problem where each instance in the data stream is classified into one or more pre-defined sets of labels. Many methods have been proposed to tackle this problem, including but not limited to ensemble-based methods. Some of these ensemble-based methods are specifically designed to work with certain multi-label base classifiers; some others employ online bagging schemes to build their ensembles. In this study, we introduce a novel online and dynamically-weighted stacked ensemble for multi-label classification, called GOOWE-ML, that utilizes spatial modeling to assign optimal weights to its component classifiers. Our model can be used with any existing incremental multi-label classification algorithm as its base classifier. We conduct experiments with 4 GOOWE-ML-based multi-label ensembles and 7 baseline models on 7 real-world datasets from diverse areas of interest. Our experiments show that GOOWE-ML ensembles yield consistently better results in terms of predictive performance in almost all of the datasets, with respect to the other prominent ensemble models.
dc.identifier.doi10.1145/3269206.3271774
dc.identifier.isbn9781450360142
dc.identifier.urihttp://hdl.handle.net/11693/50332
dc.language.isoEnglish
dc.publisherACM
dc.relation.isversionofhttps://doi.org/10.1145/3269206.3271774
dc.source.titleCIKM '18 Proceedings of the 27th ACM International Conference on Information and Knowledge Managementen_US
dc.subjectBaggingen_US
dc.subjectClassificationen_US
dc.subjectData streamen_US
dc.subjectEnsemble learningen_US
dc.subjectMulti-labelen_US
dc.subjectOnline learningen_US
dc.subjectSupervised learningen_US
dc.titleA novel online stacked ensemble for multi-label stream classificationen_US
dc.typeConference Paperen_US

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