Explainable matrix: visualization for global and local interpretability of random forest classification ensembles (2021)
- Authors:
- USP affiliated authors: PAULOVICH, FERNANDO VIEIRA - ICMC ; POPOLIN NETO, MÁRIO - ICMC
- Unidade: ICMC
- DOI: 10.1109/TVCG.2020.3030354
- Subjects: VISUALIZAÇÃO; APRENDIZADO COMPUTACIONAL
- Keywords: Random forest visualization; logic rules visualization; classification model interpretability; explainable artificial intelligence
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Publisher place: Los Alamitos
- Date published: 2021
- Source:
- Título do periódico: IEEE Transactions on Visualization and Computer Graphics
- ISSN: 1077-2626
- Volume/Número/Paginação/Ano: v. 27, n. 2, p. 1427-1437, Feb. 2021
- Este periódico é de assinatura
- Este artigo é de acesso aberto
- URL de acesso aberto
- Cor do Acesso Aberto: green
-
ABNT
POPOLIN NETO, Mário e PAULOVICH, Fernando Vieira. Explainable matrix: visualization for global and local interpretability of random forest classification ensembles. IEEE Transactions on Visualization and Computer Graphics, v. 27, n. 2, p. 1427-1437, 2021Tradução . . Disponível em: https://doi.org/10.1109/TVCG.2020.3030354. Acesso em: 29 maio 2024. -
APA
Popolin Neto, M., & Paulovich, F. V. (2021). Explainable matrix: visualization for global and local interpretability of random forest classification ensembles. IEEE Transactions on Visualization and Computer Graphics, 27( 2), 1427-1437. doi:10.1109/TVCG.2020.3030354 -
NLM
Popolin Neto M, Paulovich FV. Explainable matrix: visualization for global and local interpretability of random forest classification ensembles [Internet]. IEEE Transactions on Visualization and Computer Graphics. 2021 ; 27( 2): 1427-1437.[citado 2024 maio 29 ] Available from: https://doi.org/10.1109/TVCG.2020.3030354 -
Vancouver
Popolin Neto M, Paulovich FV. Explainable matrix: visualization for global and local interpretability of random forest classification ensembles [Internet]. IEEE Transactions on Visualization and Computer Graphics. 2021 ; 27( 2): 1427-1437.[citado 2024 maio 29 ] Available from: https://doi.org/10.1109/TVCG.2020.3030354 - Random Forest interpretability - explaining classification models and multivariate data through logic rules visualizations
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Informações sobre o DOI: 10.1109/TVCG.2020.3030354 (Fonte: oaDOI API)
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