Detalhes bibliográficos
| Ano de defesa: |
2012 |
| Autor(a) principal: |
Santana, Laura Emmanuella Alves dos Santos |
| Orientador(a): |
Canuto, Anne Magaly de Paula |
| Banca de defesa: |
Não Informado pela instituição |
| Tipo de documento: |
Tese
|
| Tipo de acesso: |
Acesso aberto |
| Idioma: |
por |
| Instituição de defesa: |
Universidade Federal do Rio Grande do Norte
|
| Programa de Pós-Graduação: |
Programa de Pós-Graduação em Sistemas e Computação
|
| Departamento: |
Ciência da Computação
|
| País: |
BR
|
| Palavras-chave em Português: |
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| Palavras-chave em Inglês: |
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| Área do conhecimento CNPq: |
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| Link de acesso: |
https://repositorio.ufrn.br/jspui/handle/123456789/17946
|
Resumo: |
Traditional applications of feature selection in areas such as data mining, machine learning and pattern recognition aim to improve the accuracy and to reduce the computational cost of the model. It is done through the removal of redundant, irrelevant or noisy data, finding a representative subset of data that reduces its dimensionality without loss of performance. With the development of research in ensemble of classifiers and the verification that this type of model has better performance than the individual models, if the base classifiers are diverse, comes a new field of application to the research of feature selection. In this new field, it is desired to find diverse subsets of features for the construction of base classifiers for the ensemble systems. This work proposes an approach that maximizes the diversity of the ensembles by selecting subsets of features using a model independent of the learning algorithm and with low computational cost. This is done using bio-inspired metaheuristics with evaluation filter-based criteria |
