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.
This research work was supported by a Spanish CICYT project number TIC2000-1056.
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Hernández-Espinosa, C., Fernández-Redondo, M., Ortiz-Gómez, M. (2003). Rule Extraction from a Multilayer Feedforward Trained Network via Interval Arithmetic Inversion. In: Mira, J., Álvarez, J.R. (eds) Computational Methods in Neural Modeling. IWANN 2003. Lecture Notes in Computer Science, vol 2686. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44868-3_79
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DOI: https://doi.org/10.1007/3-540-44868-3_79
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