On-the-fly ensemble classifier pruning in evolving data streams

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buir.advisorCan, Fazlı
dc.contributor.authorElbaşı, Sanem
dc.date.accessioned2019-09-30T11:36:14Z
dc.date.available2019-09-30T11:36:14Z
dc.date.copyright2019-09
dc.date.issued2019-09
dc.date.submitted2019-09-27
dc.departmentDepartment of Computer Engineeringen_US
dc.descriptionCataloged from PDF version of article.en_US
dc.descriptionThesis (M.S.): Bilkent University, Department of Computer Engineering, İhsan Doğramacı Bilkent University, 2019.en_US
dc.descriptionIncludes bibliographical references (leaves 40-45).en_US
dc.description.abstractEnsemble pruning is the process of selecting a subset of component classifiers from an ensemble which performs at least as well as the original ensemble while reducing storage and computational costs. Ensemble pruning in data streams is a largely unexplored area of research. It requires analysis of ensemble components as they are running on the stream and differentiation of useful classifiers from redundant ones. We present two on-the-fly ensemble pruning methods; Class-wise Component Ranking-based Pruner (CCRP) and Cover Coefficient-based Pruner (CCP). CCRP aims that the resulting pruned ensemble contains the best performing classifier for each target class and hence, reduces the effects of class imbalance. On the other hand, CCP aims to select components that make misclassification errors on different instances. The conducted experiments on real-world and synthetic data streams demonstrate that different types of ensembles that integrate pruners consume significantly less memory and perform significantly faster without hurting the predictive performance.en_US
dc.description.degreeM.S.en_US
dc.description.provenanceSubmitted by Betül Özen (ozen@bilkent.edu.tr) on 2019-09-30T11:36:14Z No. of bitstreams: 1 THESIS_ON_THE_FLY_ENSEMBLE_PRUNING_IN_EVOLVING_DATA_STREAMS.pdf: 1359609 bytes, checksum: c8d83f460b9932e4453a75101798cac5 (MD5)en
dc.description.provenanceMade available in DSpace on 2019-09-30T11:36:14Z (GMT). No. of bitstreams: 1 THESIS_ON_THE_FLY_ENSEMBLE_PRUNING_IN_EVOLVING_DATA_STREAMS.pdf: 1359609 bytes, checksum: c8d83f460b9932e4453a75101798cac5 (MD5) Previous issue date: 2019-09en
dc.description.statementofresponsibilityby Sanem Elbaşıen_US
dc.embargo.release2020-03-27
dc.format.extentxi, 48 leaves : charts (some color) ; 30 cm.en_US
dc.identifier.itemidB126693
dc.identifier.urihttp://hdl.handle.net/11693/52517
dc.language.isoEnglishen_US
dc.publisherBilkent Universityen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectEnsemble learningen_US
dc.subjectEnsemble pruningen_US
dc.subjectEnsemble efficiencyen_US
dc.subjectConcept driften_US
dc.titleOn-the-fly ensemble classifier pruning in evolving data streamsen_US
dc.title.alternativeEvrilen veri akışlarında heyet sınıflandırıcıların anında budanmasıen_US
dc.typeThesisen_US

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