QUANTIZATION METHOD TO IMPROVE THE FIDELITY OF RULE EXTRACTION ALGORITHMS FOR USE WITH ARTIFICIAL NEURAL NETWORKS

Rules for explaining the output of an ANN are derived by: creating decision trees trained to approximate the ANN and optimize a defined criterion, a threshold value for the criterion being calculated to determine for which node of the ANN the input activations should be split between branches of the decision tree; obtaining threshold value combinations each comprising a threshold value obtained for respective nodes of the ANN; for each combination, using the combination to perform a rule extraction algorithm to extract a rule explaining the output of the ANN and to obtain a fidelity metric indicating the accuracy of the rule with respect to predictions of the ANN; determining which combination yields the best fidelity metric; and using the rule extraction algorithm with the combination of threshold values determined to yield the best fidelity metric to extract at least one rule for explaining the output of the ANN.