Skip to main content

Symbolic Rule Extraction from the DIMLP Neural Network

  • Conference paper
Hybrid Neural Systems (Hybrid Neural Systems 1998)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1778))

Included in the following conference series:

Abstract

The interpretation of neural network responses as symbolic rules is actually a difficult task. Our first approach consists in characterising the discriminant hyper-plane frontiers. More particularly, we point out that the shape of a discriminant frontier built by a standard multi-layer perceptron is related to an equation with two terms. The first one is linear, and the second is logarithmic.

This equation is not sufficient to easily generate symbolic rules. So, we introduce the Discretized Interpretable Multi Layer Perceptron (DIMLP) model that is a more constrained multi-layer architecture. From this special network, rules are extracted in polynomial time and continuous attributes do not need to be binary transformed.

We apply DIMLP to three applications of the public domain in which it gives better average predictive accuracy than C4.5 algorithm and discuss rule quality.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

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

    Article  Google Scholar 

  2. Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Francisco (1993)

    Google Scholar 

  3. Quinlan, J.R.: Comparing Connectionist and Symbolic Learning Methods. In: Rivest, R. (ed.) Computational Learning Theory and Natural Learning, pp. 445–456 (1994)

    Google Scholar 

  4. Breiman, L., Friedmann, J.H., Olshen, R.A., Stone, J.: Classification and Regression Trees. Wadsworth and Brooks, Monterey, California (1984)

    MATH  Google Scholar 

  5. Maire, F.: Rule Extraction by Backpropagation of Polyhedra. Neural Networks 12(4–5), 717–725 (1999)

    Article  Google Scholar 

  6. Bologna, G.: Rule Extraction from the IMLP Neural Network: a Comparative Study. In: Proceedings of the Workshop of Rule Extraction from Trained Articial Neural Networks (after the Neural Information Processing Conference), pp. 13–19 (1996)

    Google Scholar 

  7. Rudel, R., Sangiovanni-Vincentelli, A.: Espresso-MV: Algorithms for Multiple- Valued Logic Minimisation. In: Proceedings of the Custom International Circuit Conference (CICC 1985), Portland, pp. 230–234 (1985)

    Google Scholar 

  8. Corwin, E., Logar, A., Oldham, W.: An Iterative Method for Training Multi-layer Networks with Threshold Functions. IEEE Transactions on Neural Networks Journal 5(3), 507–508 (1994)

    Article  Google Scholar 

  9. Aarts, E.H.L., Laarhoven, P.J.M.: Simulated Annealing: Theory and Applications. Kluwer Academic Publishers, Dordrecht (1987)

    MATH  Google Scholar 

  10. Thrun, S.B.: The Monk’s Problems: a Performance Comparison of Different Learning Algorithms. Technical Report, Carnegie Mellon University, CMU-CS-91-197 (1991)

    Google Scholar 

  11. Gorman, R.P., Sejnowski, T.J.: Analysis of Hidden Units in a Layered Network Trained to Classify Sonar Targets. Neural Networks 1(1), 75–88 (1988)

    Article  Google Scholar 

  12. Alimoglu, F., Alpaydin, E.: Combining Multiple Representations and Classifiers for Pen-based Handwritten Digit Recognition. In: Proceedings of the Fourth International Conference on Document Analysis and Recognition (ICDAR 1997), Ulm, Germany (1997)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2000 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Bologna, G. (2000). Symbolic Rule Extraction from the DIMLP Neural Network. In: Wermter, S., Sun, R. (eds) Hybrid Neural Systems. Hybrid Neural Systems 1998. Lecture Notes in Computer Science(), vol 1778. Springer, Berlin, Heidelberg. https://doi.org/10.1007/10719871_17

Download citation

  • DOI: https://doi.org/10.1007/10719871_17

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-67305-7

  • Online ISBN: 978-3-540-46417-4

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics