FS-SCF network: neural network interpretability based on counterfactual generation and feature selection for fault diagnosis (2024)
- Authors:
- USP affiliated authors: MARTINS, MARCELO RAMOS - EP ; BARRAZA, JOAQUÍN EDUARDO FIGUEROA - EP
- Unidade: EP
- DOI: 10.1016/j.eswa.2023.121670
- Subjects: REDES NEURAIS; FALHAS COMPUTACIONAIS; GESTÃO DA SEGURANÇA EM SISTEMAS COMPUTACIONAIS
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Source:
- Título: Expert systems with applications
- ISSN: 0957-4174
- Volume/Número/Paginação/Ano: v. 237, Part C, article number 121670, p. 1-16, Mar. 2024
- Este periódico é de assinatura
- Este artigo NÃO é de acesso aberto
- Cor do Acesso Aberto: closed
-
ABNT
FIGUEROA BARRAZA, Joaquín Eduardo e LÓPEZ DROGUETT, Enrique Andrés e MARTINS, Marcelo Ramos. FS-SCF network: neural network interpretability based on counterfactual generation and feature selection for fault diagnosis. Expert systems with applications, v. 237, p. 1-16, 2024Tradução . . Disponível em: https://doi.org/10.1016/j.eswa.2023.121670. Acesso em: 10 jan. 2026. -
APA
Figueroa Barraza, J. E., López Droguett, E. A., & Martins, M. R. (2024). FS-SCF network: neural network interpretability based on counterfactual generation and feature selection for fault diagnosis. Expert systems with applications, 237, 1-16. doi:10.1016/j.eswa.2023.121670 -
NLM
Figueroa Barraza JE, López Droguett EA, Martins MR. FS-SCF network: neural network interpretability based on counterfactual generation and feature selection for fault diagnosis [Internet]. Expert systems with applications. 2024 ; 237 1-16.[citado 2026 jan. 10 ] Available from: https://doi.org/10.1016/j.eswa.2023.121670 -
Vancouver
Figueroa Barraza JE, López Droguett EA, Martins MR. FS-SCF network: neural network interpretability based on counterfactual generation and feature selection for fault diagnosis [Internet]. Expert systems with applications. 2024 ; 237 1-16.[citado 2026 jan. 10 ] Available from: https://doi.org/10.1016/j.eswa.2023.121670 - Towards interpretable deep learning: a feature selection framework for prognostics and health management using deep neural networks
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Informações sobre o DOI: 10.1016/j.eswa.2023.121670 (Fonte: oaDOI API)
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