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Parallel Implementation of a Fuzzy Rule Based Classifier

  • Conference paper
High Performance Computing for Computational Science - VECPAR 2004 (VECPAR 2004)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3402))

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Abstract

This works presents an implementation of a fuzzy rule based classifier where each single variable fuzzy rule based classifier (or a set of them) is assigned to a different processor in a parallel architecture. Partial conclusions are synchronized and processed by a master processor. This approach has been applied to a very large database and results are compared with a parallel neural network approach.

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References

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Author information

Authors and Affiliations

  1. COPPE/Federal University of Rio de Janeiro, P.O.Box 68506, 21945-970, Rio de Janeiro, RJ, Brazil

    Alexandre G. Evsukoff, Myrian C. A. Costa & Nelson F. F. Ebecken

Authors
  1. Alexandre G. Evsukoff
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  2. Myrian C. A. Costa
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  3. Nelson F. F. Ebecken
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Editor information

Editors and Affiliations

  1. INP (ENSEEIHT); IRIT, University of Toulouse, rue Charles-Camichel, 31071, Toulouse CEDEX 7, France

    Michel Daydé

  2. Computer Science Department, University of Tennessee, 37996-3450, Knoxville, TN, USA

    Jack Dongarra

  3. EUIT Telecomunicación - DIATEL, Universidad Politécnica de Madrid, Ctra. Valencia, Km. 7, 28031, Madrid, Spain

    Vicente Hernández

  4. Faculdade de Engenharia da Universidade do Porto, Rua Dr. Roberto Frias s/n, 4200-465, Porto, Portugal

    José M. L. M. Palma

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© 2005 Springer-Verlag Berlin Heidelberg

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Evsukoff, A.G., Costa, M.C.A., Ebecken, N.F.F. (2005). Parallel Implementation of a Fuzzy Rule Based Classifier. In: Daydé, M., Dongarra, J., Hernández, V., Palma, J.M.L.M. (eds) High Performance Computing for Computational Science - VECPAR 2004. VECPAR 2004. Lecture Notes in Computer Science, vol 3402. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11403937_15

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  • DOI: https://doi.org/10.1007/11403937_15

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25424-9

  • Online ISBN: 978-3-540-31854-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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