Abstract
A major drawback of artificial neural networks is their black-box character. In this paper, we use the equivalence between artificial neural networks and a specific fuzzy rule base to extract the knowledge embedded in the network. We demonstrate this using a benchmark problem: the recognition of digits produced by a LED device. The method provides a symbolic and comprehensible description of the knowledge learned by the network during its training.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Cloete, I., Zurada, J.M. (eds.): Knowledge-Based Neurocomputing. MIT Press, Cambridge (2000)
Andrews, R., Diederich, J., Tickle, A.: Survey and critique of techniques for extracting rules from trained artificial neural networks. Knowledge-Based Systems 8, 373–389 (1995)
Tickle, A., Andrews, R., Golea, M., Diederich, J.: The truth will come to light: directions and challenges in extracting the knowledge embedded within trained artificial neural networks. IEEE Trans. Neural Networks 9, 1057–1068 (1998)
Tron, E., Margaliot, M.: Mathematical modeling of observed natural behavior: a fuzzy logic approach. Fuzzy Sets Systems 146, 437–450 (2004)
Dubois, D., Nguyen, H.T., Prade, H., Sugeno, M.: Introduction: The real contribution of fuzzy systems. In: Nguyen, H.T., Sugeno, M. (eds.) Fuzzy Systems: Modeling and Control, pp. 1–17. Kluwer, Dordrecht (1998)
Zadeh, L.A.: Fuzzy logic = computing with words. IEEE Trans. Fuzzy Systems 4, 103–111 (1996)
McGarry, K., Wermter, S., MacIntyre, J.: Hybrid neural systems: from simple coupling to fully integrated neural networks. Neural Computing Surveys 2, 62–93 (1999)
Mitra, S., Hayashi, Y.: Neuro-fuzzy rule generation: survey in soft computing framework. IEEE Trans. Neural Networks 11, 748–768 (2000)
Jang, J.S.R., Sun, C.T., Mizutani, E.: Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence. Prentice-Hall, Englewood Cliffs (1997)
Fu, L.M., Fu, L.C.: Mapping rule based systems into neural architectures. Knowledge Based Systems 3, 48–56 (1990)
Jang, J.S.R., Sun, C.T.: Functional equivalence between radial basis function networks and fuzzy inference systems. IEEE Trans. Neural Networks 4, 156–159 (1993)
Benitez, J.M., Castro, J.L., Requena, I.: Are artificial neural networks black boxes? IEEE Trans. Neural Networks 8, 1156–1164 (1997)
Castro, J.L., Mantas, C.J., Benitez, J.M.: Interpretation of artificial neural networks by means of fuzzy rules. IEEE Trans. Neural Networks 13, 101–116 (2002)
Zhang, D., Bai, X.L., Cai, K.Y.: Extended neuro-fuzzy models of multilayer perceptrons. Fuzzy Sets Systems 142, 221–242 (2004)
Kolman, E., Margaliot, M.: Are artificial neural networks white boxes? IEEE Trans. Neural Networks (to appear) (Online) Available: www.eng.tau.ac.il/~michaelm
Kolman, E., Margaliot, M.: Neural networks = fuzzy rule bases. In: Ruan, D., et al. (eds.) Applied Computational Intelligence – Proceedings of the 6th International FLINS Conference, pp. 111–117. World Scientific, Singapore (2004)
Kolman, E., Margaliot, M.: Knowledge extraction from neural networks using the all-permutations fuzzy rule base (submitted) (Online) Available: www.eng.tau.ac.il/~michaelm
Breiman, L., Friedman, J., Olshen, R., Stone, C.: Classification and Regression Trees. Wadsworth International Group, ch. 2 (1984)
Aha, D.W., Kibler, D., Albert, M.K.: Instance-based learning algorithms. Machine Learning 6, 37–66 (1991)
Boger, Z., Guterman, H.: Knowledge extraction from artificial neural networks models. In: Proc. IEEE Int. Conf. Systems, Man and Cybernetics (SMC 1997), Orlando, Florida, pp. 3030–3035 (1997)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kolman, E., Margaliot, M. (2005). Knowledge Extraction from Neural Networks Using the All-Permutations Fuzzy Rule Base: The LED Display Recognition Problem. In: Cabestany, J., Prieto, A., Sandoval, F. (eds) Computational Intelligence and Bioinspired Systems. IWANN 2005. Lecture Notes in Computer Science, vol 3512. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11494669_150
Download citation
DOI: https://doi.org/10.1007/11494669_150
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26208-4
Online ISBN: 978-3-540-32106-4
eBook Packages: Computer ScienceComputer Science (R0)