An extreme learning machine algorithm for semi-supervised classification of unbalanced data streams with concept drift-Reference-Cited by-同舟云学术

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An extreme learning machine algorithm for semi-supervised classification of unbalanced data streams with concept drift

Published:2023-10-02 Issue: Volume: Page:
ISSN:1380-7501
Container-title:Multimedia Tools and Applications
language:en
Short-container-title:Multimed Tools Appl

Author:

da Silva Carlos A. S.^ORCID,Krohling Renato A.

Funder

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior

Conselho Nacional de Desenvolvimento Científico e Tecnológico

Fundação de Amparo a Pesquisa e Inovação do Espírito Santo

Nvidia

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Hardware and Architecture,Media Technology,Software

Link

https://link.springer.com/content/pdf/10.1007/s11042-023-17039-5.pdf

Reference57 articles.

1. Agrahari S, Singh AK (2022) Concept drift detection in data stream mining: a literature review. J King Saud Univ Comput Inf Sci 34(10, Part B):9523–9540. https://doi.org/10.1016/j.jksuci.2021.11.006

2. Akusok A, Björk KM, Miche Y et al (2015) High-performance extreme learning machines: a complete toolbox for big data applications. IEEE Access 3:1011–1025. https://doi.org/10.1109/ACCESS.2015.2450498

3. Anderson R, Koh YS, Dobbie G et al (2019) Recurring concept meta-learning for evolving data streams. Expert Syst Appl 138:112832. https://doi.org/10.1016/j.eswa.2019.112832

4. Barros RSM, Santos SGTC (2018) A large-scale comparison of concept drift detectors. Inf Sci 451–452:348–370. https://doi.org/10.1016/j.ins.2018.04.014

5. de Barros RSM, Hidalgo JIG, de Lima Cabral DR (2018) Wilcoxon rank sum test drift detector. Neurocomputing 275:1954–1963. https://doi.org/10.1016/j.neucom.2017.10.051

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