Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/2222
Título: | Simultaneous evolution of neural network topologies and weights for classification and regression |
Autor(es): | Rocha, Miguel Cortez, Paulo Neves, José |
Palavras-chave: | Supervised learning Multilayer perceptrons Evolutionary algorithms Ensembles |
Data: | 8-Jun-2005 |
Editora: | Springer |
Revista: | Lecture Notes in Computer Science |
Citação: | INTERNATIONAL WORK-CONFERENCE ON ARTIFICIAL NEURAL NETWORKS (IWANN), 8, Barcelona, 2005 - "Computational intelligence and bioinspired systems : proceedings". Heidelberg : Springer, 2005. ISBN 3-540-26208-3. p. 59-66. |
Resumo(s): | Artificial Neural Networks (ANNs) are important Data Mining (DM) techniques. Yet, the search for the optimal ANN is a challenging task: the architecture should learn the input-output mapping without overfitting the data and training algorithms tend to get trapped into local minima. Under this scenario, the use of Evolutionary Computation (EC) is a promising alternative for ANN design and training. Moreover, since EC methods keep a pool of solutions, an ensemble can be build by combining the best ANNs. This work presents a novel algorithm for the optimization of ANNs, using a direct representation, a structural mutation operator and Lamarckian evolution. Sixteen real-world classification/regression tasks were used to test this strategy with single and ensemble based versions. Competitive results were achieved when compared with a heuristic model selection and other DM algorithms. |
Tipo: | Artigo em ata de conferência |
URI: | https://hdl.handle.net/1822/2222 |
ISBN: | 3-540-26208-3 |
ISSN: | 0302-9743 |
Versão da editora: | The original publication is available at http://www.springerlink.com |
Arbitragem científica: | yes |
Acesso: | Acesso aberto |
Aparece nas coleções: | DI/CCTC - Artigos (papers) DSI - Engenharia da Programação e dos Sistemas Informáticos |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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spr-59.pdf | 201,85 kB | Adobe PDF | Ver/Abrir |