Abstract
In this paper we propose a new algorithm for rule extraction from a trained Multilayer Feedforward network. The algorithm is based on an interval arithmetic network inversion for particular target outputs. The types of rules extracted are N-dimensional intervals in the input space. We have performed experiments with four databases and the results are very interesting. One rule extracted by the algorithm can cover 86% of the neural network output and in other cases 64 rules cover 100% of the neural network output.
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Hernández-Espinosa, C., Fernández-Redondo, M., Ortiz-Gómez, M. (2003). Inversion of a Neural Network via Interval Arithmetic for Rule Extraction. In: Kaynak, O., Alpaydin, E., Oja, E., Xu, L. (eds) Artificial Neural Networks and Neural Information Processing — ICANN/ICONIP 2003. ICANN ICONIP 2003 2003. Lecture Notes in Computer Science, vol 2714. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44989-2_80
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DOI: https://doi.org/10.1007/3-540-44989-2_80
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