Using Class Decomposition for Building GA with Fuzzy Rule-Based Classifiers

  • Passent El-Kafrawy
  • Amr Sauber
Part of the Studies in Computational Intelligence book series (SCI, volume 363)


A classification problem is fully partitioned into several small problems each of which is responsible for solving a fraction of the original problem. In this paper, a new approach using class-based partitioning is proposed to improve the performance of genetic-based classifiers. Rules are defined with fuzzy genes to represent variable length rules. We experimentally evaluate our approach on four different data sets and demonstrate that our algorithm can improve classification rate compared to normal Rule-based classification GAs [1] with non-partitioned techniques.


Genetic Algorithm Rule-based Classification Class Decomposition Divide and Conquer Fuzzy Rules 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Passent El-Kafrawy
    • 1
  • Amr Sauber
    • 1
  1. 1.Dept of Math and CS, Faculty of ScienceMenoufia UniversityEgypt

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