Mining Pareto-optimal counterfactual antecedents with a branch-and-boundmodel-agnostic algorithm (2024)
- Authors:
- Autor USP: NONATO, LUIS GUSTAVO - ICMC
- Unidade: ICMC
- DOI: 10.1007/s10618-022-00906-4
- Subjects: APRENDIZADO COMPUTACIONAL; ALGORITMOS
- Keywords: Explainability; Interpretability; Knowledge mining; Multi-objective optimization; Crime data; Counterfactual
- Language: Inglês
- Imprenta:
- Source:
- Título: Data Mining and Knowledge Discovery
- ISSN: 1384-5810
- Volume/Número/Paginação/Ano: v. 38, p. 2942-2974, 2024
- Este periódico é de assinatura
- Este artigo NÃO é de acesso aberto
- Cor do Acesso Aberto: closed
-
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: 01 out. 2024. -
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 2024 out. 01 ] 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 2024 out. 01 ] Available from: https://doi.org/10.1007/s10618-022-00906-4 - Inspired quadrangulation
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Informações sobre o DOI: 10.1007/s10618-022-00906-4 (Fonte: oaDOI API)
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