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Evolving Fuzzy Classifier for Data Mining - an Information Retrieval Approach

  • Pavel Krömer
  • Václav Snášel
  • Jan Platoš
  • Ajith Abraham
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 85)

Abstract

Fuzzy classifiers and fuzzy rules can be informally defined as tools that use fuzzy sets or fuzzy logic for their operations. In this paper, we use genetic programming to evolve a fuzzy classifier in the form of a fuzzy search expression to predict product quality. We interpret the data mining task as a fuzzy information retrieval problem and we apply successful information retrieval method for search query optimization to the fuzzy classifier evolution. We demonstrate the ability of genetic programming to evolve useful fuzzy classifiers on a real world case in which a classifier detecting faulty products in an industrial production process is evolved.

Keywords

Information Retrieval Genetic Programming Intrusion Detection System Information Retrieval System Query Optimization 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Pavel Krömer
    • 1
  • Václav Snášel
    • 1
  • Jan Platoš
    • 1
  • Ajith Abraham
    • 2
  1. 1.Department of Computer Science, FEECS, VŠBTechnical University of OstravaOstrava-PorubaCzech Republic
  2. 2.Machine Intelligence Research Labs (MIR Labs)Scientific Network for Innovation and Research Excellence (SNIRE)USA

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