Classification and Rule extraction is an important application of Artificial Neural Network. To extract fewer rules from multilayer feed forward neural network has been a research area. The internal representation of the network is augmented by a distance term to extract fewer rules from the feed
forward neural network and experimented on five datasets. Understanding affect of different factors of the dataset and network on extraction of a number of rules from the network can reveal important pieces of information which may help researchers to enhance the rule extraction process. This work investigates the internal behavior of neural network in rule extraction process on five different dataset.
Keywords: Rule extraction, Feed Forward Neural Network, Hidden units, Activation value, Hidden neurons.