Symbolic Rule Extraction From Neural Networks: An Application to Identifying Organizations Adopting IT

Abstract

Interest in the application of neural networks as tools for decision support has been growing in recent years. A major drawback often associated with neural networks is the difficulty in understanding the knowledge represented by a trained network. This paper describes an approach that can extract symbolic rules from neural networks. We illustrate how the approach successfully extracted rules from a data set collected from a survey of the service sectors in the United Kingdom. The extracted rules were then used to distinguish between organizations using computers from those that do not. The classification scheme based on these rules was used to identify specific segments of a market for promoting adoption of information technology. The extracted rules are not only concise but also outperform discriminant analysis in terms of predictive accuracy.

Keywords: Backpropagation Algorithm, Neural Networks, Symbolic Rules, Technology Adoption

JEL Classification: C45, C38, M15