Compressed k-nearest neighbors ensembles for evolving data streams (2020)
- Authors:
- Autor USP: MELLO, RODRIGO FERNANDES DE - ICMC
- Unidade: ICMC
- DOI: 10.3233/FAIA200189
- Subjects: ANÁLISE DE SÉRIES TEMPORAIS; APRENDIZADO COMPUTACIONAL; MINERAÇÃO DE DADOS; ALGORITMOS ÚTEIS E ESPECÍFICOS
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Source:
- Título: Frontiers in Artificial Intelligence and Applications
- ISSN: 0922-6389
- Volume/Número/Paginação/Ano: v. 325, p. 961-968, 2020
- Conference titles: European Conference on Artificial Intelligence - ECAI
- Este periódico é de acesso aberto
- Este artigo NÃO é de acesso aberto
-
ABNT
BAHRI, Maroua et al. Compressed k-nearest neighbors ensembles for evolving data streams. Frontiers in Artificial Intelligence and Applications. Amsterdam: IOS Press. Disponível em: https://doi.org/10.3233/FAIA200189. Acesso em: 22 jan. 2026. , 2020 -
APA
Bahri, M., Bifet, A., Maniu, S., Mello, R. F. de, & Tziortziotis, N. (2020). Compressed k-nearest neighbors ensembles for evolving data streams. Frontiers in Artificial Intelligence and Applications. Amsterdam: IOS Press. doi:10.3233/FAIA200189 -
NLM
Bahri M, Bifet A, Maniu S, Mello RF de, Tziortziotis N. Compressed k-nearest neighbors ensembles for evolving data streams [Internet]. Frontiers in Artificial Intelligence and Applications. 2020 ; 325 961-968.[citado 2026 jan. 22 ] Available from: https://doi.org/10.3233/FAIA200189 -
Vancouver
Bahri M, Bifet A, Maniu S, Mello RF de, Tziortziotis N. Compressed k-nearest neighbors ensembles for evolving data streams [Internet]. Frontiers in Artificial Intelligence and Applications. 2020 ; 325 961-968.[citado 2026 jan. 22 ] Available from: https://doi.org/10.3233/FAIA200189 - A stable and online approach to detect concept drift in data streams
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Informações sobre o DOI: 10.3233/FAIA200189 (Fonte: oaDOI API)
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