Classification with multi-modal classes using evolutionary algorithms and constrained clustering (2018)
- Authors:
- Autor USP: HRUSCHKA, EDUARDO RAUL - ICMC
- Unidade: ICMC
- DOI: 10.1109/CEC.2018.8477858
- Subjects: APRENDIZADO COMPUTACIONAL; ALGORITMOS GENÉTICOS; COMPUTAÇÃO EVOLUTIVA
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Publisher: IEEE
- Publisher place: Piscataway
- Date published: 2018
- Source:
- Título: Proceedings
- Conference titles: IEEE Congress on Evolutionary Computation - CEC
- Este periódico é de acesso aberto
- Este artigo NÃO é de acesso aberto
-
ABNT
COVÕES, Thiago Ferreira e HRUSCHKA, Eduardo Raul. Classification with multi-modal classes using evolutionary algorithms and constrained clustering. 2018, Anais.. Piscataway: IEEE, 2018. Disponível em: https://doi.org/10.1109/CEC.2018.8477858. Acesso em: 13 fev. 2026. -
APA
Covões, T. F., & Hruschka, E. R. (2018). Classification with multi-modal classes using evolutionary algorithms and constrained clustering. In Proceedings. Piscataway: IEEE. doi:10.1109/CEC.2018.8477858 -
NLM
Covões TF, Hruschka ER. Classification with multi-modal classes using evolutionary algorithms and constrained clustering [Internet]. Proceedings. 2018 ;[citado 2026 fev. 13 ] Available from: https://doi.org/10.1109/CEC.2018.8477858 -
Vancouver
Covões TF, Hruschka ER. Classification with multi-modal classes using evolutionary algorithms and constrained clustering [Internet]. Proceedings. 2018 ;[citado 2026 fev. 13 ] Available from: https://doi.org/10.1109/CEC.2018.8477858 - On the influence of imputation in classification: practical issues
- An evolutionary algorithm for clustering data streams with a variable number of clusters
- An Experimental Study on Unsupervised Clustering-Based Feature Selection Methods
- Using both latent and supervised shared topics for multitask learning
- A study of K-means-based algorithms for constrained clustering
- Biocom_Usp: tweet sentiment analysis with adaptive boosting ensemble
- A semi-supervised approach to estimate the number of clusters per class
- A distance-based mutation operator for learning bayesian network structures using evolutionary algorithms
- Evolving Gaussian mixture models with splitting and merging mutation operators
- A cluster-based feature selection approach
Informações sobre o DOI: 10.1109/CEC.2018.8477858 (Fonte: oaDOI API)
How to cite
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
