EACImpute: an evolutionary algorithm for clustering-based imputation (2009)
- Autores:
- Autor USP: HRUSCHKA, EDUARDO RAUL - ICMC
- Unidade: ICMC
- DOI: 10.1109/ISDA.2009.86
- Assunto: INTELIGÊNCIA ARTIFICIAL
- Idioma: Inglês
- Imprenta:
- Editora: IEEE Computer Society
- Local: Los Alamitos
- Data de publicação: 2009
- ISBN: 9781424447350
- Fonte:
- Título do periódico: Proceedings
- Nome do evento: International Conference on Intelligent Systems Design and Applications - ISDA
- Este periódico é de assinatura
- Este artigo NÃO é de acesso aberto
- Cor do Acesso Aberto: closed
-
ABNT
SILVA, Jonathan de Andrade e HRUSCHKA, Eduardo Raul. EACImpute: an evolutionary algorithm for clustering-based imputation. 2009, Anais.. Los Alamitos: IEEE Computer Society, 2009. Disponível em: https://doi.org/10.1109/ISDA.2009.86. Acesso em: 24 abr. 2024. -
APA
Silva, J. de A., & Hruschka, E. R. (2009). EACImpute: an evolutionary algorithm for clustering-based imputation. In Proceedings. Los Alamitos: IEEE Computer Society. doi:10.1109/ISDA.2009.86 -
NLM
Silva J de A, Hruschka ER. EACImpute: an evolutionary algorithm for clustering-based imputation [Internet]. Proceedings. 2009 ;[citado 2024 abr. 24 ] Available from: https://doi.org/10.1109/ISDA.2009.86 -
Vancouver
Silva J de A, Hruschka ER. EACImpute: an evolutionary algorithm for clustering-based imputation [Internet]. Proceedings. 2009 ;[citado 2024 abr. 24 ] Available from: https://doi.org/10.1109/ISDA.2009.86 - An experimental study on the use of nearest neighbor-based imputation algorithms for classification tasks
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- An Experimental Study on Unsupervised Clustering-Based Feature Selection Methods
- On the influence of imputation in classification: practical issues
- Towards improving cluster-based feature selection with a simplified silhouette filter
- Document clustering for forensic computing: an approach for improving computer inspection
- Document clustering for forensic analysis: an approach for improving computer inspection
- Evolving Gaussian mixture models with splitting and merging mutation operators
- An optimization framework for combining ensembles of classifiers and clusterers with applications to nontransductive semisupervised learning and transfer learning
Informações sobre o DOI: 10.1109/ISDA.2009.86 (Fonte: oaDOI API)
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