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Ibero-American Conference on Artificial Intelligence

IBERAMIA 2002: Advances in Artificial Intelligence — IBERAMIA 2002 pp 440–449Cite as

Rule Extraction from Radial Basis Function Networks by Using Support Vectors

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2527))

Abstract

In this paper, a procedure for rule extraction from radial basis function networks (RBFNs) is proposed. The algorithm is based on the use of a support vector machine (SVM) as a frontier pattern selector. By using geometric methods, centers of the RBF units are combined with support vectors in order to construct regions (ellipsoids or hyper-rectangles) in the input space, which are later translated to if-then rules. Additionally, the support vectors are used to determine overlapping between classes and to refine the rule base. The experimental results indicate that a very high fidelity between RBF network and the extracted set of rules can be achieved with low overlapping between classes.

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References

  1. Andrews, R., Diederich, J., Tickle, A.: A Survey and Critique of Techniques for Extracting Rules from Trained Artificial Neural Networks. Knowledge-Based Systems. 8(6) (1995) 373–389

    Article  Google Scholar 

  2. Bishop, C.: Neural Networks for Pattern Recognition. Oxford University Press, Inc., New York (1995)

    Google Scholar 

  3. Brotherton, T., Chadderdon, G., Grabill, P.: Automated Rule Extraction for Engine Vibration Analysis. Proc. 1999 IEEE Aerospace Conference. Vol. 3 (1999) 29–38

    Google Scholar 

  4. Cortes, C., Vapnik, V.: Support-Vector Networks. Machine Learning. 20 (1995) 237–297.

    Google Scholar 

  5. Craven, M. Shavlik, J.: Using Neural Networks for Data Mining. Future Generation Computer Systems. 13 (1997) 211–229

    Article  Google Scholar 

  6. Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines and other kernel-based learning methods. Cambridge University Press, Cambridge (2000)

    Google Scholar 

  7. Domingos, P.: Unifying Instance-Based and rule-Based Induction. Machine Learning. 24 (1991) 141–168

    Google Scholar 

  8. Duda, R. O., Hart, P. E., Stork, D.G.: Pattern Recognition. John Wiley & Sons, Inc., New York (2001)

    Google Scholar 

  9. Fu, X., Wang, L.: Rule Extraction by Genetic Algorithms Based on a Simplified RBF Neural Network. Proc. of the 2001 Congress on Evolutionary Computation. Vol. 2 (2001) 753–758

    Google Scholar 

  10. Girosi, F., Poggio, T.: Networks and the best approximation property. Biological Cybernetics. 63 (1990) 169–176

    Article  MATH  MathSciNet  Google Scholar 

  11. Haykin, S.: Neural Networks: A comprehensive foundation. Macmillan, New York (1994)

    MATH  Google Scholar 

  12. Huber, K., Berthold, M.: Building Precise Classifiers with Automatic Rule Extraction. Proc. IEEE International Conference on Neural Networks. Vol 3 (1995) 1263–1268

    Google Scholar 

  13. McGarry, K., Wermter, S., MacIntyre, J.: Knowledge Extraction from Local Function Networks. Proc. IJCAI. (2001) 765–770

    Google Scholar 

  14. Merz, C. J., Murphy, P. M.: UCI Repository for Machine Learning Data-Bases. Irvine, CA: University of California, Department of Information and Computer Science, http://www.ics.uci.edu/~mlearn/MLRepository.html] (1998)

  15. Mitra, S., Pal, S. K., Mitra, P.: Data Mining in Soft Computing Framework: A survey. IEEE Transactions on Neural Networks. 13 (1) (2002) 3–14.

    Article  Google Scholar 

  16. Moody, J., Darken, C.J.: Fast Learning in Networks of Locally-Tuned Processing Units. Neural Computation. 1 (1989) 281–294

    Article  Google Scholar 

  17. Park, J., Sandberg, I.W.: Universal approximation using radial basis function networks. Neural Computation. 3 (2) (1991) 246–257

    Article  Google Scholar 

  18. Roger, J.-S., Sun, C.-T.: Functional Equivalence Between Radial Basis Function Networks and Fuzzy Inference Systems. IEEE Transactions on Neural Networks. 4 (1) (1993) 156–158

    Article  Google Scholar 

  19. Salzberg, S.: A Nearest Hyperrectangle Learning Method. Machine Learning. 6 (1991) 251–276

    Google Scholar 

  20. Tickle, A., Maire, F., Bologna, G., Andrews, R.; Diederich, J.: Lessons from Past, Current Issues, and Future Research Directions in Extracting the Knowledge Embedded Artificial Neural Networks,.In: Wermter, S., Sun, R. (eds): Hybrid Neural Systems. Springer-Verlag, Berlin (2000)

    Google Scholar 

  21. Tickle. A., Andrews, R., Mostefa, G., Diederich, J.: The Truth will come to light: Directions and Challenges in Extracting the Knowledge Embedded within Trained Artificial Neural Networks. IEEE Transactions on Neural Networks. 9 (6) (1998) 1057–1068

    Article  Google Scholar 

  22. Vapnik, V.: Statistical Learning Theory. John Wiley&Sons, Inc., New York (1998)

    MATH  Google Scholar 

  23. Wermter, S. Sun, R. (eds): Hybrid Neural Systems. Springer-Verlag, Berlin (2000)

    Google Scholar 

  24. Witten, I. H., Frank, E.: Data Mining. Practical Machine Learning Tools and Techniques with Java Implementations. Academic Press, San Diego (1999)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Dept. of Systems Engineering, Polytechnical University of Catalonia, E-08800, Vilanova i la Geltrú, Spain

    Haydemar Núñez & Haydemar Núñez

  2. LEA-SICA, European Associated Lab. Intelligent Systems and Advanced Control, Rambla de l’Exposió s/n, E-08028, Vilanova i la Geltrú, Spain

    Cecilio Angulo & Cecilio Angulo

  3. Facultad de Ciencias, Universidad Central de Venezuela, Escuela de Computación, Caracas, Venezuela

    Andreu Català & Andreu Català

Authors
  1. Haydemar Núñez
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  2. Cecilio Angulo
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  3. Andreu Català
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Editor information

Editors and Affiliations

  1. Telefónica Investigación y Desarrollo, Emilio Vargas 6, 28043, Madrid, Spain

    Francisco J. Garijo

  2. Dpto. Lenguajes y Sistemas Informáticos, Universidad de Sevilla, ETS Ingeniería Informática, 41012, Seville, Spain

    José C. Riquelme  & Miguel Toro  & 

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© 2002 Springer-Verlag Berlin Heidelberg

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Núñez, H., Angulo, C., Català, A. (2002). Rule Extraction from Radial Basis Function Networks by Using Support Vectors. In: Garijo, F.J., Riquelme, J.C., Toro, M. (eds) Advances in Artificial Intelligence — IBERAMIA 2002. IBERAMIA 2002. Lecture Notes in Computer Science(), vol 2527. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36131-6_45

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  • DOI: https://doi.org/10.1007/3-540-36131-6_45

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-00131-7

  • Online ISBN: 978-3-540-36131-2

  • eBook Packages: Springer Book Archive

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