Unsupervised concept drift detection for multi-label data streams

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buir.contributor.authorGülcan, Ege Berkay
buir.contributor.authorCan, Fazlı
buir.contributor.orcidGülcan, Ege Berkay|0000-0003-1237-0829
dc.citation.epage2434en_US
dc.citation.spage2401en_US
dc.citation.volumeNumber56en_US
dc.contributor.authorGülcan, Ege Berkay
dc.contributor.authorCan, Fazlı
dc.date.accessioned2023-02-17T11:12:51Z
dc.date.available2023-02-17T11:12:51Z
dc.date.issued2022-07-17
dc.departmentDepartment of Computer Engineeringen_US
dc.description.abstractMany real-world applications adopt multi-label data streams as the need for algorithms to deal with rapidly changing data increases. Changes in data distribution, also known as concept drift, cause existing classification models to rapidly lose their effectiveness. To assist the classifiers, we propose a novel algorithm called Label Dependency Drift Detector (LD3), an unsupervised concept drift detector using label dependencies within the data for multi-label data streams. Our study exploits the dynamic temporal dependencies between labels using a label influence ranking method, which leverages a data fusion algorithm and uses the produced ranking to detect concept drift. LD3 is the first unsupervised concept drift detection algorithm in the multi-label classification problem area. In this study, we perform an extensive evaluation of LD3 by comparing it with 14 prevalent supervised concept drift detection algorithms that we adapt to the problem area using 15 datasets and a baseline classifier. The results show that LD3 provides between 16.9 and 56% better predictive performance than comparable detectors on both real-world and synthetic data streams. © 2022, The Author(s), under exclusive licence to Springer Nature B.V.en_US
dc.identifier.doi10.1007/s10462-022-10232-2en_US
dc.identifier.issn026-92821
dc.identifier.urihttp://hdl.handle.net/11693/111506
dc.publisherSpringeren_US
dc.relation.isversionofhttps://doi.org/10.1007/s10462-022-10232-2en_US
dc.source.titleArtificial Intelligence Reviewen_US
dc.subjectBig dataen_US
dc.subjectConcept driften_US
dc.subjectDrift detectionen_US
dc.subjectMulti-label classificationen_US
dc.subjectMulti-label data streamen_US
dc.titleUnsupervised concept drift detection for multi-label data streamsen_US
dc.typeArticleen_US

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