Implicit concept drift detection for multi-label data streams

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buir.advisorCan, Fazlı
dc.contributor.authorGülcan, Ege Berkay
dc.date.accessioned2022-02-04T05:42:27Z
dc.date.available2022-02-04T05:42:27Z
dc.date.copyright2022-01
dc.date.issued2022-01
dc.date.submitted2022-02-01
dc.descriptionCataloged from PDF version of article.en_US
dc.descriptionThesis (Master's): Bilkent University, Department of Computer Engineering, İhsan Doğramacı Bilkent University, 2022.en_US
dc.descriptionIncludes bibliographical references (leaves 40-46).en_US
dc.description.abstractMany real-world applications adopt multi-label data streams as the need for algo-rithms to deal with rapidly generated data increases. For such streams, changes in data distribution, also known as concept drift, cause the existing classification models to rapidly lose their effectiveness. To assist the classifiers, we propose a novel algorithm called Label Dependency Drift Detector (LD3), an implicit (un-supervised) 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 12 datasets and a baseline classifier. The results show that LD3 provides between 19.8% and 68.6% better predictive performance than comparable detectors on both real-world and synthetic data streams.en_US
dc.description.provenanceSubmitted by Betül Özen (ozen@bilkent.edu.tr) on 2022-02-04T05:42:26Z No. of bitstreams: 1 Implicit concept drift detection for multi-label data streams.pdf: 2886557 bytes, checksum: 2d4ca8d5a3645990e33576dca41d0d90 (MD5)en
dc.description.provenanceMade available in DSpace on 2022-02-04T05:42:27Z (GMT). No. of bitstreams: 1 Implicit concept drift detection for multi-label data streams.pdf: 2886557 bytes, checksum: 2d4ca8d5a3645990e33576dca41d0d90 (MD5) Previous issue date: 2022-01en
dc.description.statementofresponsibilityby Ege Berkay Gülcanen_US
dc.format.extentx, 46 leaves : charts ; 30 cm.en_US
dc.identifier.itemidB160760
dc.identifier.urihttp://hdl.handle.net/11693/76999
dc.language.isoEnglishen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectBig dataen_US
dc.subjectMulti-label data streamen_US
dc.subjectMulti-label classificationen_US
dc.subjectConcept driften_US
dc.subjectDrift detectionen_US
dc.subjectData fusionen_US
dc.titleImplicit concept drift detection for multi-label data streamsen_US
dc.title.alternativeÇok etiketli veri akısları için denetimsiz kavram kayma tespitien_US
dc.typeThesisen_US
thesis.degree.disciplineComputer Engineering
thesis.degree.grantorBilkent University
thesis.degree.levelMaster's
thesis.degree.nameMS (Master of Science)

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