Enhancing random forest for continuous data streams using divergence measures to select decision trees (2025)
- Authors:
- Autor USP: SILVA, DIEGO FURTADO - ICMC
- Unidade: ICMC
- DOI: 10.54808/IMCIC2025.01.127
- Subjects: APRENDIZADO COMPUTACIONAL; ANÁLISE DE SÉRIES TEMPORAIS
- Keywords: Dynamic Tree Selection; Divergence Measure; Data Stream Classification; Random Forest; Decision Tree
- Language: Inglês
- Imprenta:
- Publisher: International Institute of Informatics and Systemics - IIIS
- Publisher place: Winter Garden
- Date published: 2025
- Source:
- Título: Proceedings
- ISSN: 2771-5914
- Conference titles: International Multi-Conference on Complexity, Informatics and Cybernetics - IMCIC
- Este periódico é de acesso aberto
- Este artigo NÃO é de acesso aberto
-
ABNT
SANTOS, Danilo Rodrigues dos e SILVA, Diego Furtado. Enhancing random forest for continuous data streams using divergence measures to select decision trees. 2025, Anais.. Winter Garden: International Institute of Informatics and Systemics - IIIS, 2025. Disponível em: https://doi.org/10.54808/IMCIC2025.01.127. Acesso em: 10 fev. 2026. -
APA
Santos, D. R. dos, & Silva, D. F. (2025). Enhancing random forest for continuous data streams using divergence measures to select decision trees. In Proceedings. Winter Garden: International Institute of Informatics and Systemics - IIIS. doi:10.54808/IMCIC2025.01.127 -
NLM
Santos DR dos, Silva DF. Enhancing random forest for continuous data streams using divergence measures to select decision trees [Internet]. Proceedings. 2025 ;[citado 2026 fev. 10 ] Available from: https://doi.org/10.54808/IMCIC2025.01.127 -
Vancouver
Santos DR dos, Silva DF. Enhancing random forest for continuous data streams using divergence measures to select decision trees [Internet]. Proceedings. 2025 ;[citado 2026 fev. 10 ] Available from: https://doi.org/10.54808/IMCIC2025.01.127 - Large scale similarity-based time series mining
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Informações sobre o DOI: 10.54808/IMCIC2025.01.127 (Fonte: oaDOI API)
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