Evolving text stream classification with a novel neural ensemble architecture

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buir.advisorCan, Fazlı
dc.contributor.authorGhahramanian, Pouya
dc.date.accessioned2022-01-28T07:51:56Z
dc.date.available2022-01-28T07:51:56Z
dc.date.copyright2022-01
dc.date.issued2022-01
dc.date.submitted2022-01-27
dc.departmentDepartment of Computer Engineeringen_US
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 39-45).en_US
dc.description.abstractWe study on-the-fly classification of evolving text streams in which the relation between the input data target labels changes over time—i.e. “concept drift”. These variations decrease the model’s performance, as predictions become less accurate over-time and they necessitate a more adaptable system. We introduce Adaptive Neural Ensemble Network (AdaNEN ), a novel ensemble-based neural approach, capable of handling concept drift in text streams. With our novel architecture, we address some of the problems neural models face when exploited for online adaptive learning environments. The problem of evolving text stream classification is relatively unexplored and most existing studies address concept drift detection and handling in numerical streams. We hypothesize that the lack of public and large-scale experimental data could be one reason. To this end, we propose a method based on an existing approach for generating evolving text streams by inducing various types of concept drifts to real-world text datasets. We provide an extensive evaluation of our proposed approach using 12 stateof- the-art baselines and eight datasets. Our experimental results show that our proposed method, AdaNEN, consistently outperforms the existing approaches in terms of predictive performance with conservative efficiency.en_US
dc.description.degreeM.S.en_US
dc.description.statementofresponsibilityby Pouya Ghahramanianen_US
dc.format.extentx, 45 leaves : charts (some color) ; 30 cm.en_US
dc.identifier.itemidB133385
dc.identifier.urihttp://hdl.handle.net/11693/76860
dc.language.isoEnglishen_US
dc.publisherBilkent Universityen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectText stream classificationen_US
dc.subjectConcept driften_US
dc.subjectEnsemble learningen_US
dc.subjectNeural networksen_US
dc.titleEvolving text stream classification with a novel neural ensemble architectureen_US
dc.title.alternativeYeni bir sinir topluluğu mimarisi ile gelişen metin akışı sınıflandırmasıen_US
dc.typeThesisen_US
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