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. © Springer-Verlag Berlin Heidelberg 2003.
CITATION STYLE
Hernández-Espinosa, C., Fernández-Redondo, M., & Ortiz-Gómez, M. (2003). Inversion of a neural network via interval arithmetic for rule extraction. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2714, 670–677. https://doi.org/10.1007/3-540-44989-2_80
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