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Filtros : "Data Mining and Knowledge Discovery" "ICMC-SME" Limpar

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Artigo de periodico
Mining Pareto-optimal counterfactual antecedents with a branch-and-boundmodel-agnostic algorithm (2024)
Raimundo, Marcos M ; Nonato, Luis Gustavo ; Poco, Jorge
Source: Data Mining and Knowledge Discovery. Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, ALGORITMOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
RAIMUNDO, Marcos M e NONATO, Luis Gustavo e POCO, Jorge. Mining Pareto-optimal counterfactual antecedents with a branch-and-boundmodel-agnostic algorithm. Data Mining and Knowledge Discovery, v. 38, p. 2942-2974, 2024Tradução . . Disponível em: https://doi.org/10.1007/s10618-022-00906-4. Acesso em: 12 nov. 2025.
APA
Raimundo, M. M., Nonato, L. G., & Poco, J. (2024). Mining Pareto-optimal counterfactual antecedents with a branch-and-boundmodel-agnostic algorithm. Data Mining and Knowledge Discovery, 38, 2942-2974. doi:10.1007/s10618-022-00906-4
NLM
Raimundo MM, Nonato LG, Poco J. Mining Pareto-optimal counterfactual antecedents with a branch-and-boundmodel-agnostic algorithm [Internet]. Data Mining and Knowledge Discovery. 2024 ; 38 2942-2974.[citado 2025 nov. 12 ] Available from: https://doi.org/10.1007/s10618-022-00906-4
Vancouver
Raimundo MM, Nonato LG, Poco J. Mining Pareto-optimal counterfactual antecedents with a branch-and-boundmodel-agnostic algorithm [Internet]. Data Mining and Knowledge Discovery. 2024 ; 38 2942-2974.[citado 2025 nov. 12 ] Available from: https://doi.org/10.1007/s10618-022-00906-4
Artigo de periodico
Better trees: an empirical study on hyperparameter tuning of classification decision tree induction algorithms (2024)
Mantovani, Rafael Gomes ; Horváth, Tomás ; Rossi, André Luis Debiaso ; Cerri, Ricardo ; Barbon Júnior, Sylvio ; Vanschoren, Joaquin ; Carvalho, André Carlos Ponce de Leon Ferreira de
Source: Data Mining and Knowledge Discovery. Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, ALGORITMOS ÚTEIS E ESPECÍFICOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
MANTOVANI, Rafael Gomes et al. Better trees: an empirical study on hyperparameter tuning of classification decision tree induction algorithms. Data Mining and Knowledge Discovery, v. 38, n. 3, p. 1364-1416, 2024Tradução . . Disponível em: https://doi.org/10.1007/s10618-024-01002-5. Acesso em: 12 nov. 2025.
APA
Mantovani, R. G., Horváth, T., Rossi, A. L. D., Cerri, R., Barbon Júnior, S., Vanschoren, J., & Carvalho, A. C. P. de L. F. de. (2024). Better trees: an empirical study on hyperparameter tuning of classification decision tree induction algorithms. Data Mining and Knowledge Discovery, 38( 3), 1364-1416. doi:10.1007/s10618-024-01002-5
NLM
Mantovani RG, Horváth T, Rossi ALD, Cerri R, Barbon Júnior S, Vanschoren J, Carvalho ACP de LF de. Better trees: an empirical study on hyperparameter tuning of classification decision tree induction algorithms [Internet]. Data Mining and Knowledge Discovery. 2024 ; 38( 3): 1364-1416.[citado 2025 nov. 12 ] Available from: https://doi.org/10.1007/s10618-024-01002-5
Vancouver
Mantovani RG, Horváth T, Rossi ALD, Cerri R, Barbon Júnior S, Vanschoren J, Carvalho ACP de LF de. Better trees: an empirical study on hyperparameter tuning of classification decision tree induction algorithms [Internet]. Data Mining and Knowledge Discovery. 2024 ; 38( 3): 1364-1416.[citado 2025 nov. 12 ] Available from: https://doi.org/10.1007/s10618-024-01002-5

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