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EFuNN Ensembles Construction Using a Clustering Method and a Coevolutionary Multi-objective Genetic Algorithm

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Neural Information Processing (ICONIP 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4234))

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Abstract

This paper presents the experiments which where made with the Clustering and Coevolution to Construct Neural Network Ensemble (CONE) approach on two classification problems and two time series prediction problems. This approach was used to create a particular type of Evolving Fuzzy Neural Network (EFuNN) ensemble and optimize its parameters using a Coevolutionary Multi-objective Genetic Algorithm. The results of the experiments reinforce some previous results which have shown that the approach is able to generate EFuNN ensembles with accuracy either better or equal to the accuracy of single EFuNNs generated without using coevolution. Besides, the execution time of CONE to generate EFuNN ensembles is lower than the execution time to produce single EFuNNs without coevolution.

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Author information

Authors and Affiliations

  1. Informatics Center, Federal University of Pernambuco, Recife-PE, P.O.Box 7851, 50732-970, Brazil

    Fernanda L. Minku & Teresa B. Ludermir

Authors
  1. Fernanda L. Minku
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  2. Teresa B. Ludermir
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Editor information

Editors and Affiliations

  1. Dept. of Computer Science and Engineering, The Chinese Univ. of Hong Kong, Shatin, N.T., Hong Kong

    Irwin King

  2. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong, New Territories,, China

    Jun Wang

  3. The Chinese University of Hong Kong, Shatin, N.T., Hong Kong

    Lai-Wan Chan

  4. Department of Computer Science and Engineering & Center for Cognitive Science, The Ohio State University, OH 43210, Columbus

    DeLiang Wang

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© 2006 Springer-Verlag Berlin Heidelberg

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Minku, F.L., Ludermir, T.B. (2006). EFuNN Ensembles Construction Using a Clustering Method and a Coevolutionary Multi-objective Genetic Algorithm. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4234. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893295_97

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  • DOI: https://doi.org/10.1007/11893295_97

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-46484-6

  • Online ISBN: 978-3-540-46485-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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