On the need of class ratio insensitive drift tests for data streams (2018)
- Authors:
- Autor USP: BATISTA, GUSTAVO ENRIQUE DE ALMEIDA PRADO ALVES - ICMC
- Unidade: ICMC
- Subjects: APRENDIZADO COMPUTACIONAL; ANÁLISE DE SÉRIES TEMPORAIS; RECONHECIMENTO DE PADRÕES
- Keywords: Class imbalance; concept drift; quantification
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Publisher: Microtome Publishing
- Publisher place: Brookline
- Date published: 2018
- Source:
- Título: Proceedings of Machine Learning Research : PMLR
- ISSN: 1938-7228
- Volume/Número/Paginação/Ano: v. 94, p. 110-124, 2018
- Conference titles: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Database - ECML PKDD
-
ABNT
MALETZKE, André Gustavo et al. On the need of class ratio insensitive drift tests for data streams. Proceedings of Machine Learning Research : PMLR. Brookline: Microtome Publishing. Disponível em: http://proceedings.mlr.press/v94/maletzke18a.html. Acesso em: 22 fev. 2026. , 2018 -
APA
Maletzke, A. G., Reis, D. dos, Cherman, E. A., & Batista, G. E. de A. P. A. (2018). On the need of class ratio insensitive drift tests for data streams. Proceedings of Machine Learning Research : PMLR. Brookline: Microtome Publishing. Recuperado de http://proceedings.mlr.press/v94/maletzke18a.html -
NLM
Maletzke AG, Reis D dos, Cherman EA, Batista GE de APA. On the need of class ratio insensitive drift tests for data streams [Internet]. Proceedings of Machine Learning Research : PMLR. 2018 ; 94 110-124.[citado 2026 fev. 22 ] Available from: http://proceedings.mlr.press/v94/maletzke18a.html -
Vancouver
Maletzke AG, Reis D dos, Cherman EA, Batista GE de APA. On the need of class ratio insensitive drift tests for data streams [Internet]. Proceedings of Machine Learning Research : PMLR. 2018 ; 94 110-124.[citado 2026 fev. 22 ] Available from: http://proceedings.mlr.press/v94/maletzke18a.html - Time series classification with motifs and characteristics
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