Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/63271
Título: | A neuroevolutionary approach to feature selection using multiobjective evolutionary algorithms |
Autor(es): | Pinto, Renê Souza Costa, M. Fernanda P. Costa, Lino Gaspar-Cunha, A. |
Palavras-chave: | Neuroevolution Multiobjective Optimization Feature Selection |
Data: | 2019 |
Editora: | Universidade do Minho. Departamento de Engenharia de Polímeros (DEP) |
Resumo(s): | Feature selection plays a central role in predictive analysis where datasets have hundreds or thousands of variables available. It can also reduce the overall training time and the computational costs of the classifiers used. However, feature selection methods can be computationally intensive or dependent of human expertise to analyze data. This study proposes a neuroevolutionary approach which uses multiobjective evolutionary algorithms to optimize neural network parameters in order to find the best network able to identify the most important variables of analyzed data. Classification is done through a Support Vector Machine (SVM) classifier where specific parameters are also optimized. The method is applied to datasets with different number of features and classes. |
Tipo: | Artigo em ata de conferência |
URI: | https://hdl.handle.net/1822/63271 |
Arbitragem científica: | yes |
Acesso: | Acesso aberto |
Aparece nas coleções: |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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artigoEUROGEN_2019_Pinto et al.pdf | 632,07 kB | Adobe PDF | Ver/Abrir |