Model performance prediction: a meta-learning approach for concept drift detection (2023)
- Authors:
- Autor USP: CARVALHO, ANDRÉ CARLOS PONCE DE LEON FERREIRA DE - ICMC
- Unidade: ICMC
- DOI: 10.1007/978-3-031-40725-3_5
- Subjects: APRENDIZADO COMPUTACIONAL; PREVISÃO (ANÁLISE DE SÉRIES TEMPORAIS)
- Keywords: Data Streams; Concept Drift; Meta Learning
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Source:
- Título do periódico: Lecture Notes in Artificial Intelligence
- ISSN: 0302-9743
- Volume/Número/Paginação/Ano: v. 14001, p. 51-62, 2023
- Conference titles: International Conference on Hybrid Artificial Intelligence Systems - HAIS
- Este periódico é de assinatura
- Este artigo NÃO é de acesso aberto
- Cor do Acesso Aberto: closed
-
ABNT
MELO, Fernanda Amaral et al. Model performance prediction: a meta-learning approach for concept drift detection. Lecture Notes in Artificial Intelligence. Cham: Springer. Disponível em: https://doi.org/10.1007/978-3-031-40725-3_5. Acesso em: 03 jun. 2024. , 2023 -
APA
Melo, F. A., Carvalho, A. C. P. de L. F. de, Lorena, A. C., & Garcia, L. P. F. (2023). Model performance prediction: a meta-learning approach for concept drift detection. Lecture Notes in Artificial Intelligence. Cham: Springer. doi:10.1007/978-3-031-40725-3_5 -
NLM
Melo FA, Carvalho ACP de LF de, Lorena AC, Garcia LPF. Model performance prediction: a meta-learning approach for concept drift detection [Internet]. Lecture Notes in Artificial Intelligence. 2023 ; 14001 51-62.[citado 2024 jun. 03 ] Available from: https://doi.org/10.1007/978-3-031-40725-3_5 -
Vancouver
Melo FA, Carvalho ACP de LF de, Lorena AC, Garcia LPF. Model performance prediction: a meta-learning approach for concept drift detection [Internet]. Lecture Notes in Artificial Intelligence. 2023 ; 14001 51-62.[citado 2024 jun. 03 ] Available from: https://doi.org/10.1007/978-3-031-40725-3_5 - Reduction strategies for hierarchical multi-label classification in protein function prediction
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Informações sobre o DOI: 10.1007/978-3-031-40725-3_5 (Fonte: oaDOI API)
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