  Artificial neural networks have been successfully applied to a variety of
business application problems involving classification and regression. Although
backpropagation neural networks generally predict better than decision trees do
for pattern classification problems, they are often regarded as black boxes,
i.e., their predictions are not as interpretable as those of decision trees. In
many applications, it is desirable to extract knowledge from trained neural
networks so that the users can gain a better understanding of the solution.
This paper presents an efficient algorithm to extract rules from artificial
neural networks. We use two-phase training algorithm for backpropagation
learning. In the first phase, the number of hidden nodes of the network is
determined automatically in a constructive fashion by adding nodes one after
another based on the performance of the network on training data. In the second
phase, the number of relevant input units of the network is determined using
pruning algorithm. The pruning process attempts to eliminate as many
connections as possible from the network. Relevant and irrelevant attributes of
the data are distinguished during the training process. Those that are relevant
will be kept and others will be automatically discarded. From the simplified
networks having small number of connections and nodes we may easily able to
extract symbolic rules using the proposed algorithm. Extensive experimental
results on several benchmarks problems in neural networks demonstrate the
effectiveness of the proposed approach with good generalization ability.
