Experimental Study of Evolutionary Based Method of Rule Extraction from Neural Networks in Medical Data

  • Urszula Markowska-Kaczmar
  • Rafal Matkowski
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4065)


In the paper the method of rule extraction from neural networks based on evolutionary approach, called GEX, is presented. Its details are described but the main stress is focussed on the experimental studies, the aim of which was to examine its usefulness in knowledge discovery and rule extraction for classification task of medical data. The tests were made using the well-known benchmark data sets from UCI, as well as two other data sets collected by Lower Silesian Oncology Center.


Neural Network Knowledge Discovery Rule Extraction Default Rule Cervix Uterus 
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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  1. 1.
    Fu, L.M.: Rule generation from neural network. IEEE Transactions on Systems, Man and Cybernetics 24, 1114–1124 (1994)CrossRefGoogle Scholar
  2. 2.
    Lu, H., Setiono, R., Liu, H.: Neurorule: A connectionist approach to datamining. In: Proc. 21st Conference on very Large Databases, Zurich (1995)Google Scholar
  3. 3.
    Taha, I., Ghosh, J.: Symbolic interpretation of artificial neural networks. Technical report, The Computer and Vision Research Center, University of Texas, Austin (1996)Google Scholar
  4. 4.
    Setiono, R., Thong, J.: An approach to generate rules from neural networks for regression problems. European Journal of Operational Research 155, 239–250 (2004)MATHCrossRefGoogle Scholar
  5. 5.
    Palade, V., Neagu, D.-C., Patton, R.J.: Interpretation of trained neural networks by rule extraction. In: Reusch, B. (ed.) Fuzzy Days 2001. LNCS, vol. 2206, pp. 152–161. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  6. 6.
    Thrun, S.B.: Extracting rules from artificial neural networks with distributed representation, advances. Neural Information Processing Systems, 7 (1995)Google Scholar
  7. 7.
    Andrews, R., Diederich, J., Tickle, A.: A survey and critique of techniques for extracting rules from trained neural networks. Knowledge-Based Systems 8, 373–389 (1995)CrossRefGoogle Scholar
  8. 8.
    Craven, M., Shavlik, J.: Extracting tree-structured representations of trained networks. In: Advances Information Processes Systems, vol. 8, MIT Press, Cambridge (1996)Google Scholar
  9. 9.
    Vinterbo, S., Ohno-Machado, L.: A genetic algorithm approach to multi-disorder diagnosis. Artificial Intelligence in Medicine 18, 117–132 (2000)CrossRefGoogle Scholar
  10. 10.
    Francisci, D., Brisson, L., Collard, M.: A scalar evolutionary approach to rule extraction. Technical report, ISRN I3S/RR-200312-FR (2003)Google Scholar
  11. 11.
    Fidelis, M., Lopes, H.S., Freitas, A.: Discovering comprehensible classification rules with genetic algorithm. In: Proc. Congress on Evolutionary Computation (CEC 2000), pp. 805–810 (2001)Google Scholar
  12. 12.
    Arbatli, D.A., Akin, L.H.: Rule extraction from trained neural network using genetic algorithm. Nonlinear Analysis, Theory Methods and Application 30, 1639–1648 (1997)MATHCrossRefGoogle Scholar
  13. 13.
    Murphy, P.M., Aha, D.W.: UCI repository of machine learning databases. Ph.D thesis, Department of Information and Computer Science, University of California, Irvine, CA (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Urszula Markowska-Kaczmar
    • 1
  • Rafal Matkowski
    • 1
  1. 1.Wroclaw University of Technology, Medical University of WroclawPoland

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