X-Processes: process model discovery with the best balance among fitness, precision, simplicity, and generalization through a genetic algorithm (2023)
- Authors:
- USP affiliated authors: FANTINATO, MARCELO - EACH ; PERES, SARAJANE MARQUES - EACH
- Unidade: EACH
- DOI: 10.1016/j.is.2023.102247
- Assunto: GENERALIZAÇÃO (APRENDIZAGEM)
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Source:
- Título do periódico: Information Systems: databases: their creation, management and utilization
- ISSN: 0306-4379
- Volume/Número/Paginação/Ano: v. 119, p. 01-25, Oct. 2023
- Este periódico é de assinatura
- Este artigo NÃO é de acesso aberto
- Cor do Acesso Aberto: closed
-
ABNT
FANTINATO, Marcelo e PERES, Sarajane Marques e REIJERS, Hajo Alexander. X-Processes: process model discovery with the best balance among fitness, precision, simplicity, and generalization through a genetic algorithm. Information Systems: databases: their creation, management and utilization, v. 119, p. 01-25, 2023Tradução . . Disponível em: https://doi.org/10.1016/j.is.2023.102247. Acesso em: 02 jun. 2024. -
APA
Fantinato, M., Peres, S. M., & Reijers, H. A. (2023). X-Processes: process model discovery with the best balance among fitness, precision, simplicity, and generalization through a genetic algorithm. Information Systems: databases: their creation, management and utilization, 119, 01-25. doi:10.1016/j.is.2023.102247 -
NLM
Fantinato M, Peres SM, Reijers HA. X-Processes: process model discovery with the best balance among fitness, precision, simplicity, and generalization through a genetic algorithm [Internet]. Information Systems: databases: their creation, management and utilization. 2023 ; 119 01-25.[citado 2024 jun. 02 ] Available from: https://doi.org/10.1016/j.is.2023.102247 -
Vancouver
Fantinato M, Peres SM, Reijers HA. X-Processes: process model discovery with the best balance among fitness, precision, simplicity, and generalization through a genetic algorithm [Internet]. Information Systems: databases: their creation, management and utilization. 2023 ; 119 01-25.[citado 2024 jun. 02 ] Available from: https://doi.org/10.1016/j.is.2023.102247 - Enhancing completion time prediction through attribute selection
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Informações sobre o DOI: 10.1016/j.is.2023.102247 (Fonte: oaDOI API)
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