Extraction of the association rules from artificial neural networks based on the multiobjective optimization

Artificial Neural Network (ANN) is one of the powerful techniques of machine learning. It has shown its effectiveness in both prediction and classification problems. However, in some fields there is still some reticence towards their use mainly the fact that they do not justify their answers. The lack of transparency on how ANN makes decisions motivated us to develop our rule extraction algorithm that extracts comprehensible rules with high accuracy and high fidelity. The aim is to generate a set of rules that mimic the decision of ANN and cover a larger set of patterns. The obtained rule sets should satisfy a well-balanced trade-off between the fidelity, the accuracy and the comprehensibility. The proposed algorithm consists of a three steps: ANN learning phase, rule extraction phase and rule simplification phase. The rule extraction phase is based on the extraction of the association rules while the rules simplification procedure is based on the laws of Boolean algebra. To evaluate the performance of our algorithm, the system has been studied using four datasets, and then compared with other rule extraction methods. The results show that our proposal offers a small set of rules having the highest accuracy and fidelity values.