Geographic context-based stacking learning for election prediction from socio-economic data (2022)
- Authors:
- USP affiliated authors: SILVA, TIAGO PINHO DA - ICMC ; PARMEZAN, ANTONIO RAFAEL SABINO - ICMC
- Unidade: ICMC
- DOI: 10.1007/978-3-031-21686-2_44
- Subjects: APRENDIZADO COMPUTACIONAL; COMPORTAMENTO ELEITORAL
- Keywords: Ensemble learning; Metalearning; Preferential voting; Spatial dependence
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Source:
- Título do periódico: Lecture Notes in Artificial Intelligence
- ISSN: 0302-9743
- Volume/Número/Paginação/Ano: v. 13653, p. 641-657, 2022
- Conference titles: Brazilian Conference on Intelligent Systems - BRACIS
- 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. Geographic context-based stacking learning for election prediction from socio-economic data. Lecture Notes in Artificial Intelligence. Cham: Springer. Disponível em: https://doi.org/10.1007/978-3-031-21686-2_44. Acesso em: 30 abr. 2024. , 2022 -
APA
Silva, T. P. da, Parmezan, A. R. S., & Batista, G. E. de A. P. A. (2022). Geographic context-based stacking learning for election prediction from socio-economic data. Lecture Notes in Artificial Intelligence. Cham: Springer. doi:10.1007/978-3-031-21686-2_44 -
NLM
Silva TP da, Parmezan ARS, Batista GE de APA. Geographic context-based stacking learning for election prediction from socio-economic data [Internet]. Lecture Notes in Artificial Intelligence. 2022 ; 13653 641-657.[citado 2024 abr. 30 ] Available from: https://doi.org/10.1007/978-3-031-21686-2_44 -
Vancouver
Silva TP da, Parmezan ARS, Batista GE de APA. Geographic context-based stacking learning for election prediction from socio-economic data [Internet]. Lecture Notes in Artificial Intelligence. 2022 ; 13653 641-657.[citado 2024 abr. 30 ] Available from: https://doi.org/10.1007/978-3-031-21686-2_44 - A graph-based spatial cross-validation approach for assessing models learned with selected features to understand election results
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Informações sobre o DOI: 10.1007/978-3-031-21686-2_44 (Fonte: oaDOI API)
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