GOOWE-ML: a novel online stacked ensemble for multi-label classification in data streams

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buir.advisorCan, Fazlı
dc.contributor.authorBüyükçakır, Alican
dc.date.accessioned2019-08-02T07:02:45Z
dc.date.available2019-08-02T07:02:45Z
dc.date.copyright2019-07
dc.date.issued2019-07
dc.date.submitted2019-07-30
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 44-50).en_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 ensemblebased 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 multilabel 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.en_US
dc.description.degreeM.S.en_US
dc.description.provenanceSubmitted by Betül Özen (ozen@bilkent.edu.tr) on 2019-08-02T07:02:45Z No. of bitstreams: 1 Buyukcakir_Alican_21200815_CS_2019_MSThesis_Bilkent.pdf: 1000605 bytes, checksum: 4f2c602676f7c19231643cbbbb3c4e56 (MD5)en
dc.description.provenanceMade available in DSpace on 2019-08-02T07:02:45Z (GMT). No. of bitstreams: 1 Buyukcakir_Alican_21200815_CS_2019_MSThesis_Bilkent.pdf: 1000605 bytes, checksum: 4f2c602676f7c19231643cbbbb3c4e56 (MD5) Previous issue date: 2019-07en
dc.description.statementofresponsibilityby Alican Büyükçakıren_US
dc.format.extentxi, 51 leaves ; 30 cm.en_US
dc.identifier.itemidB160108
dc.identifier.urihttp://hdl.handle.net/11693/52288
dc.language.isoEnglishen_US
dc.publisherBilkent Universityen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectMulti-labelen_US
dc.subjectData streamen_US
dc.subjectSupervised learningen_US
dc.subjectClassificationen_US
dc.subjectEnsemble learningen_US
dc.subjectStackingen_US
dc.titleGOOWE-ML: a novel online stacked ensemble for multi-label classification in data streamsen_US
dc.title.alternativeGOOWE-ML: veri akışlarında çok-etiketli siniflandirma için yeni bir üst-öğrenicili çoklu-sınıflandırıcıen_US
dc.typeThesisen_US

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Dept. of Computer Engineering - Master's degree