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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© 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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