A graph-based spatial cross-validation approach for assessing models learned with selected features to understand election results (2021)
- Authors:
- USP affiliated authors: SILVA, TIAGO PINHO DA - ICMC ; PARMEZAN, ANTONIO RAFAEL SABINO - ICMC
- Unidade: ICMC
- DOI: 10.1109/ICMLA52953.2021.00150
- Subjects: MINERAÇÃO DE DADOS; APRENDIZADO COMPUTACIONAL; PREVISÃO (ANÁLISE DE SÉRIES TEMPORAIS); ELEIÇÕES DIRETAS
- Keywords: spatial dependence; spatial autocorrelation; spatial data partitioning; semivariogram; prediction error
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Publisher: IEEE
- Publisher place: Piscataway
- Date published: 2021
- Source:
- Título do periódico: Proceedings
- Conference titles: IEEE International Conference on Machine Learning and Applications - ICMLA
- Este periódico é de assinatura
- Este artigo NÃO é de acesso aberto
- Cor do Acesso Aberto: closed
-
ABNT
SILVA, Tiago Pinho da e PARMEZAN, Antonio Rafael Sabino e BATISTA, Gustavo Enrique de Almeida Prado Alves. A graph-based spatial cross-validation approach for assessing models learned with selected features to understand election results. 2021, Anais.. Piscataway: IEEE, 2021. Disponível em: https://doi.org/10.1109/ICMLA52953.2021.00150. Acesso em: 05 jun. 2024. -
APA
Silva, T. P. da, Parmezan, A. R. S., & Batista, G. E. de A. P. A. (2021). A graph-based spatial cross-validation approach for assessing models learned with selected features to understand election results. In Proceedings. Piscataway: IEEE. doi:10.1109/ICMLA52953.2021.00150 -
NLM
Silva TP da, Parmezan ARS, Batista GE de APA. A graph-based spatial cross-validation approach for assessing models learned with selected features to understand election results [Internet]. Proceedings. 2021 ;[citado 2024 jun. 05 ] Available from: https://doi.org/10.1109/ICMLA52953.2021.00150 -
Vancouver
Silva TP da, Parmezan ARS, Batista GE de APA. A graph-based spatial cross-validation approach for assessing models learned with selected features to understand election results [Internet]. Proceedings. 2021 ;[citado 2024 jun. 05 ] Available from: https://doi.org/10.1109/ICMLA52953.2021.00150 - Geographic context-based stacking learning for election prediction from socio-economic data
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Informações sobre o DOI: 10.1109/ICMLA52953.2021.00150 (Fonte: oaDOI API)
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