A new rule extraction algorithm based on interval arithmetic
نویسندگان
چکیده
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 database 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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