Rules Extraction from Trained Neural Networks Using Decision Trees: Comparison of Different Rules Extraction Algorithms

Artificial neural networks(ANN)are very efficient in solving various kinds of problems. But Lack of explanation capability (Black box nature of Neural Networks)is one of the most important reasons why Artificial Neural Networks do not get necessary interest in some parts of industry. In this book we provide an efficient approach to overcome the black box nature of Artificial neural networks. In this approach Artificial neural networks first trained and then combined with decision trees in order to fetch knowledge learn in the training process. After successful training knowledge is extracted from these trained neural networks using decision trees in the forms of IF THEN Rules which we can easily understand as compare to direct neural network outputs. Weka machine learning simulator with version 3.7.5 and Matlab version R2010a is used for experimental purpose. The experimental study is done on bank customer's data which have 12 attributes and 600 instances. The results study show that although neural networks takes much time in training and testing but are more accurate in classification then Decision Trees