A New Pruning Technique for the Fuzzy ARTMAP Neural Network and Its Application to Medical Decision Support

摘要

This paper describes a neural network-based classification tool that can be deployed for data-based decision support tasks. In particular, the Fuzzy ARTMAP (FAM) network is investigated, and a new pruning technique is proposed. The pruning technique is implemented successively to eliminate those rarely activated nodes in the category layer of FAM. Three data sets with different characteristics are used to analyze its effectiveness. In addition, a benchmark medical problem is used to evaluate its applicability as a decision support tool for medical diagnosis. From the experiment, the pruning technique is able to improve classification performances, as compared with those of to the original FAM network, as well as other machine learning methods. More importantly, the pruning technique yields more stable performances with fewer nodes, and results in a more parsimonious FAM network for undertaking data classification and decision support tasks.