Knowledge Extraction From Trained Neural Networks

Received Jul 16 th , 2012 Revised Aug 01 th , 2012 Accepted Sept 02 th , 2012 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 work artificial neural networks first trained and then combined with decision trees in order to fetch knowledge learnt 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. We use decision trees to train on the results set of trained neural network and compare the performance of neural networks and decision trees in knowledge extraction from neural networks. Weka machine learning simulator with version 3.7.5 is used for research purpose. The experimental study is done on bank customers‟ 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. Keyword: