Abstract
In this paper, we address computational complexity issues of decompositional approaches to if-then rule extraction from feed-forward neural networks. We also introduce a computationally efficient technique based on ordered-attributes. It reduces search space significantly and finds valid and general rules for single nodes in the networks. Empirical results are shown.
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References
Andrews, Robert, Diederich, Joachim, Tickle, Alam B.: Survey and critique of techniques for extracting rules from trained artificial neural networks. Knowledge-Based Systems 8(6) (1995) 373–389
Fu, LiMin.: Knowledge-based connectionism for revising domain theories. IEEE Transactions on Systems, Man, and Cybernetics 23(1) (1993) 173–182
Fu, LiMin.: Neural Networks in Computer Intelligence. McGraw Hill, Inc., (1994)
Fu, LiMin.: Rule generation from neural networks. IEEE Transactions on Systems, Man, and Cybernetics 24(8) (1994) 1114–1124
Fu, LiMin, Kim, Hyeoncheol.: Abstraction and Representation of Hidden Knowl-edge in an Adapted Neural Network. unpublished, CISE, University of Florida (1994)
Gallant, S.I.: Connectionist expert systems. Communications of the ACM 31(2) (1988) 152–169
Taha, Ismali A., Ghosh, Joydeep: Symbolic interpretation of artificial neural net-works. IEEE Transactions on Knowledge and Data Engineering 11(3) (1999) 443–463
Setino, Rudy, Liu, Huan: Understanding neural networks via rule extraction. Pro-ceedings of the 14th International Conference on Neural Networks. (1) Montreal, Canada (1995) 480–485
Towell, Geoffrey G., Shavlik, Jude W.: Extracting refined rules from knowledge-based neural networks. Machine Learning 13(1) (1993)
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© 2000 Springer-Verlag Berlin Heidelberg
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Kim, H. (2000). Computationally Efficient Heuristics for If-Then Rule Extraction from Feed-Forward Neural Networks. In: Arikawa, S., Morishita, S. (eds) Discovery Science. DS 2000. Lecture Notes in Computer Science(), vol 1967. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44418-1_14
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DOI: https://doi.org/10.1007/3-540-44418-1_14
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Online ISBN: 978-3-540-44418-3
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