EACImpute: an evolutionary algorithm for clustering-based imputation (2009)
- Authors:
- Autor USP: HRUSCHKA, EDUARDO RAUL - ICMC
- Unidade: ICMC
- DOI: 10.1109/ISDA.2009.86
- Assunto: INTELIGÊNCIA ARTIFICIAL
- Language: Inglês
- Imprenta:
- Publisher: IEEE Computer Society
- Publisher place: Los Alamitos
- Date published: 2009
- ISBN: 9781424447350
- Source:
- Título: Proceedings
- Conference titles: International Conference on Intelligent Systems Design and Applications - ISDA
- Este periódico é de acesso aberto
- Este artigo NÃO é de acesso aberto
-
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: 20 jan. 2026. -
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 2026 jan. 20 ] 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 2026 jan. 20 ] Available from: https://doi.org/10.1109/ISDA.2009.86 - An optimization framework for combining ensembles of classifiers and clusterers with applications to nontransductive semisupervised learning and transfer learning
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- An evolutionary algorithm for missing values substitution in classification tasks
- Towards improving cluster-based feature selection with a simplified silhouette filter
- Agrupamento de documentos aplicado à computação forense: uma abordagem para aperfeiçoar análises periciais de computadores
- A privacy-aware Bayesian approach for combining classifier and cluster ensembles
- Computação forense via agrupamento hierárquico de documentos
- Extending k-means-based algorithms for evolving data streams with variable number of clusters
- Splitting and merging Gaussian mixture model components: an evolutionary approach
- Evolving Gaussian mixture models with splitting and merging mutation operators
Informações sobre o DOI: 10.1109/ISDA.2009.86 (Fonte: oaDOI API)
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