Unsupervised concept drift detection with a discriminative classifier

dc.citation.epage2368en_US
dc.citation.spage2365en_US
dc.contributor.authorGözüaçık, Ömeren_US
dc.contributor.authorBüyükçakır, Alicanen_US
dc.contributor.authorBonab, H.en_US
dc.contributor.authorCan, Fazlıen_US
dc.contributor.bilkentauthorGözüaçık, Ömer
dc.contributor.bilkentauthorBüyükçakır, Alican
dc.contributor.bilkentauthorCan, Fazlı
dc.coverage.spatialBeijing, Chinaen_US
dc.date.accessioned2020-01-30T12:01:51Z
dc.date.available2020-01-30T12:01:51Z
dc.date.issued2019
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.departmentDepartment of Computer Engineeringen_US
dc.descriptionDate of Conference: 3-7 November 2019en_US
dc.descriptionConference Name: 28th ACM International Conference on Information and Knowledge Management, CIKM 2019en_US
dc.description.abstractIn data stream mining, one of the biggest challenges is to develop algorithms that deal with the changing data. As data evolve over time, static models become outdated. This phenomenon is called concept drift, and it is investigated extensively in the literature. Detecting and subsequently adapting to concept drifts yield more robust and better performing models. In this study, we present an unsupervised method called D3 which uses a discriminative classifier with a sliding window to detect concept drift by monitoring changes in the feature space. It is a simple method that can be used along with any existing classifier that does not intrinsically have a drift adaptation mechanism. We experiment on the most prevalent concept drift detectors using 8 datasets. The results demonstrate that D3 outperforms the baselines, yielding models with higher performances on both real-world and synthetic datasets.en_US
dc.description.sponsorshipACM SIGIRen_US
dc.description.sponsorshipACM SIGWEBen_US
dc.identifier.doi10.1145/3357384.3358144en_US
dc.identifier.isbn9781450369763
dc.identifier.urihttp://hdl.handle.net/11693/52934
dc.language.isoEnglishen_US
dc.publisherAssociation for Computing Machineryen_US
dc.relation.isversionofhttps://dx.doi.org/10.1145/3357384.3358144en_US
dc.source.titleProceedings of the 28th ACM International Conference on Information and Knowledge Management, CIKM 2019en_US
dc.subjectData streamen_US
dc.subjectConcept driften_US
dc.subjectDrift detectionen_US
dc.titleUnsupervised concept drift detection with a discriminative classifieren_US
dc.typeConference Paperen_US
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