BELS: a broad ensemble learning system for data stream classification

buir.advisorCan, Fazlı
dc.contributor.authorBakhshi, Sepehr
dc.date.accessioned2022-01-28T08:14:52Z
dc.date.available2022-01-28T08:14:52Z
dc.date.copyright2021-12
dc.date.issued2021-12
dc.date.submitted2022-01-07
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, 2021.en_US
dc.descriptionIncludes bibliographical references (leaves 48-54).en_US
dc.description.abstractData stream classification has become a major research topic due to the increase in temporal data. One of the biggest hurdles of data stream classification is the development of algorithms that deal with evolving data, also known as concept drifts. As data changes over time, static prediction models lose their validity. Adapting to concept drifts provides more robust and better performing models. The Broad Learning System (BLS) is an effective broad neural architecture recently developed for incremental learning. BLS cannot provide instant response since it requires huge data chunks and is unable to handle concept drifts. We propose a Broad Ensemble Learning System (BELS) for stream classification with concept drift. BELS uses a novel updating method that greatly improves bestin- class model accuracy. It employs a dynamic output ensemble layer to address the limitations of BLS. We present its mathematical derivation, provide comprehensive experiments with 11 datasets that demonstrate the adaptability of our model, including a comparison of our model with BLS, and provide parameter and robustness analysis on several drifting streams, showing that it statistically significantly outperforms seven state-of-the-art baselines. We show that our proposed method improves on average 44% compared to BLS, and 29% compared to other competitive baselines.en_US
dc.description.degreeM.S.en_US
dc.description.statementofresponsibilityby Sepehr Bakhshien_US
dc.format.extentx, 54 leaves : charts ; 30 cm.en_US
dc.identifier.itemidB082777
dc.identifier.urihttp://hdl.handle.net/11693/76862
dc.language.isoEnglishen_US
dc.publisherBilkent Universityen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectData stream miningen_US
dc.subjectConcept driften_US
dc.subjectEnsemble learningen_US
dc.subjectNeural networksen_US
dc.subjectBig dataen_US
dc.titleBELS: a broad ensemble learning system for data stream classificationen_US
dc.title.alternativeBELS: veri akışı sınıflandırması için geniş bir topluluk öğrenim sistemien_US
dc.typeThesisen_US
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