Generating new patterns for information gain and improved neural network learning

This paper introduces an approach to generate new patterns for improved neural network training. The patterns are based on the information obtained by means of a rule extraction approach. In this way, the training process is re-iterated using the most informative patterns. The data generation process is further enhanced by incorporating the high quality rules obtained from a decision tree. Results indicate that the approach results in improved generalization, especially in difficult to learn domains