A cluster-based feature selection approach (2009)
- Authors:
- Autor USP: HRUSCHKA, EDUARDO RAUL - ICMC
- Unidade: ICMC
- DOI: 10.1007/978-3-642-02319-4_20
- Assunto: INTELIGÊNCIA ARTIFICIAL
- Language: Inglês
- Imprenta:
- Source:
- Título: Lecture Notes in Artificial Intelligence
- ISSN: 0302-9743
- Volume/Número/Paginação/Ano: v. 5572, p. 169-176, 2009
- Conference titles: International Conference Hybrid Artificial Intelligence Systems - HAIS
- Este artigo NÃO possui versão em acesso aberto
-
Status: Nenhuma versão em acesso aberto identificada -
ABNT
COVÕES, Thiago Ferreira et al. A cluster-based feature selection approach. Lecture Notes in Artificial Intelligence. Berlin: Springer. Disponível em: https://doi.org/10.1007/978-3-642-02319-4_20. Acesso em: 11 mar. 2026. , 2009 -
APA
Covões, T. F., Hruschka, E. R., Castro, L. N. de, & Santos, Á. M. dos. (2009). A cluster-based feature selection approach. Lecture Notes in Artificial Intelligence. Berlin: Springer. doi:10.1007/978-3-642-02319-4_20 -
NLM
Covões TF, Hruschka ER, Castro LN de, Santos ÁM dos. A cluster-based feature selection approach [Internet]. Lecture Notes in Artificial Intelligence. 2009 ; 5572 169-176.[citado 2026 mar. 11 ] Available from: https://doi.org/10.1007/978-3-642-02319-4_20 -
Vancouver
Covões TF, Hruschka ER, Castro LN de, Santos ÁM dos. A cluster-based feature selection approach [Internet]. Lecture Notes in Artificial Intelligence. 2009 ; 5572 169-176.[citado 2026 mar. 11 ] Available from: https://doi.org/10.1007/978-3-642-02319-4_20 - 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
- Classification with multi-modal classes using evolutionary algorithms and constrained clustering
- A distance-based mutation operator for learning bayesian network structures using evolutionary algorithms
- Evolving Gaussian mixture models with splitting and merging mutation operators
Informações sobre a disponibilidade de versões do artigo em acesso aberto coletadas automaticamente via oaDOI API (Unpaywall).
How to cite
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
