Symbolic interpretation of artificial neural networks using genetic algorithms

Abstract

The knowledge acquired during the learning of artificial neural networks (ANNs) is coded as values in synapticweights, which makes their interpretations difficult, hence the name of the black box. The aim of this work is to providea comprehensible interpretation of the ANN’s decisions by extracting symbolic rules. We improve the performance of ourextraction algorithm by combining the ANN with a genetic algorithm. Misleading rules whose support and confidencevalues are less than fixed thresholds are removed and, as a result, the comprehensibility is improved. The extracted rulesare evaluated and compared with other works. The results show good performance of our proposal in terms of fidelityand accuracy