dc.contributor.advisor | Can, Fazlı | |
dc.contributor.author | Bakhshi, Sepehr | |
dc.date.accessioned | 2022-01-28T08:14:52Z | |
dc.date.available | 2022-01-28T08:14:52Z | |
dc.date.copyright | 2021-12 | |
dc.date.issued | 2021-12 | |
dc.date.submitted | 2022-01-07 | |
dc.identifier.uri | http://hdl.handle.net/11693/76862 | |
dc.description | Cataloged from PDF version of article. | en_US |
dc.description | Thesis (Master's): Bilkent University, Department of Computer Engineering, İhsan Doğramacı Bilkent University, 2021. | en_US |
dc.description | Includes bibliographical references (leaves 48-54). | en_US |
dc.description.abstract | Data 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.statementofresponsibility | by Sepehr Bakhshi | en_US |
dc.format.extent | x, 54 leaves : charts ; 30 cm. | en_US |
dc.language.iso | English | en_US |
dc.rights | info:eu-repo/semantics/openAccess | en_US |
dc.subject | Data stream mining | en_US |
dc.subject | Concept drift | en_US |
dc.subject | Ensemble learning | en_US |
dc.subject | Neural networks | en_US |
dc.subject | Big data | en_US |
dc.title | BELS: a broad ensemble learning system for data stream classification | en_US |
dc.title.alternative | BELS: veri akışı sınıflandırması için geniş bir topluluk öğrenim sistemi | en_US |
dc.type | Thesis | en_US |
dc.department | Department of Computer Engineering | en_US |
dc.publisher | Bilkent University | en_US |
dc.description.degree | M.S. | en_US |
dc.identifier.itemid | B082777 | |