Generating new patterns for information gain and improved neural network learning
Herna Lydia Viktor · 2000
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.