Interval arithmetic inversion: a new rule extraction algorithm
Carlos Hernández-Espinosa, Mercedes Fernández-Redondo, Mamen Ortiz-Gómez · 2004
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 sixty four rules cover 100% of the neural network output.