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)


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.


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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Crestani, F., Pasi, G.: Soft information retrieval: Applications of fuzzy set theory and neural networks. In: Neuro-Fuzzy Techniques for Intelligent Information Systems, pp. 287–315. Springer, Heidelberg (1999)Google Scholar
  2. 2.
    Kraft, D.H., Petry, F.E., Buckles, B.P., Sadasivan, T.: Genetic Algorithms for Query Optimization in Information Retrieval: Relevance Feedback. In: Genetic Algorithms and Fuzzy Logic Systems. World Scientific, Singapore (1997)Google Scholar
  3. 3.
    Larsen, H.L.: Retrieval evaluation. In: Modern Information Retrieval Course. Aalborg University Esbjerg (2004)Google Scholar
  4. 4.
    Losee, R.M.: When information retrieval measures agree about the relative quality of document rankings. Journal of the American Society of Information Science 51(9), 834–840 (2000)CrossRefGoogle Scholar
  5. 5.
    Koza, J.: Genetic programming: A paradigm for genetically breeding populations of computer programs to solve problems. Dept. of Computer Science, Stanford University, Technical Report STAN-CS-90-1314 (1990)Google Scholar
  6. 6.
    Mitchell, M.: An Introduction to Genetic Algorithms. MIT Press, Cambridge (1996)Google Scholar
  7. 7.
    Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)zbMATHGoogle Scholar
  8. 8.
    Affenzeller, M., Winkler, S., Wagner, S., Beham, A.: Genetic Algorithms and Genetic Programming: Modern Concepts and Practical Applications. Chapman & Hall, Boca Raton (2009)zbMATHCrossRefGoogle Scholar
  9. 9.
    Húsek, D., Owais, S.S.J., Snášel, V., Krömer, P.: Boolean queries optimization by genetic programming. Neural Network World 15(5), 359–409 (2005)Google Scholar
  10. 10.
    Snasel, V., Abraham, A., Owais, S., Platos, J., Kromer, P.: User Profiles Modeling in Information Retrieval Systems. In: Emergent Web Intelligence: Advanced Information Retrieval, pp. 169–198. Springer, London (2010)CrossRefGoogle Scholar
  11. 11.
    Salton, G., Buckley, C.: Term-weighting approaches in automatic text retrieval. Information Processing and Management 24(5), 513–523 (1988)CrossRefGoogle Scholar

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

Personalised recommendations