A new rule extraction algorithm based on interval arithmetic

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.

• Type: Conference
• Year: 2003
• Where: ESANN
• Authors: Carlos Hernández-Espinosa, Mercedes Fernández-Redondo, Mamen Ortiz-Gómez
• Keywords: Algorithm, ESANN 2003, ESANN 2007, Multilayer Feedforward Network, Neural Network Output