Paired k-NN learners with dynamically adjusted number of neighbors for classification of drifting data streams-Reference-Cited by-同舟云学术

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Paired k-NN learners with dynamically adjusted number of neighbors for classification of drifting data streams

Published:2022-12-30 Issue:4 Volume:65 Page:1787-1816
ISSN:0219-1377
Container-title:Knowledge and Information Systems
language:en
Short-container-title:Knowl Inf Syst

Author:

Hidalgo Juan Isidro González,Santos Silas Garrido T. C.,de Barros Roberto Souto Maior

Funder

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior

Conselho Nacional de Desenvolvimento Científico e Tecnológico

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Hardware and Architecture,Human-Computer Interaction,Information Systems,Software

Link

https://link.springer.com/content/pdf/10.1007/s10115-022-01817-y.pdf

Reference53 articles.

1. Agrawal R, Imielinski T, Swami A (1993) Database mining: a performance perspective. IEEE Trans Knowl Data Eng 5(6):914–925

2. Alberghini G, Barbon Junior S, Cano A (2022) Adaptive ensemble of self-adjusting nearest neighbor subspaces for multi-label drifting data streams. Neurocomputing 481:228–248

3. Almeida PR, Oliveira LS, Britto AS Jr et al (2018) Adapting dynamic classifier selection for concept drift. Expert Syst Appl 104:67–85

4. Atkeson CG, Moore AW, Schaal S (1997) Locally weighted learning. Artif Intell Rev 11(1–5):11–73

5. Barddal JP, Gomes HM, Granatyr J et al (2016) Overcoming feature drifts via dynamic feature weighted k-nearest neighbor learning. In: Proceedings of 23rd IEEE international conference on pattern recognition (ICPR), pp 2186–2191

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