A data-driven rule-based neural network model for classification

Abstract

A novel approach for generating rules from neural networks is proposed. Rather than extracting rules from a trained general neural network, we use a neural network structure which permits rules to be more readily interpreted. This network incorporates logic neurons, with a combination of both fixed and adaptive weights. The backpropagation learning rules is adapted to reflect the new architecture. The proposed model also provides an opportunity for encoding expert rules and combining these rules with data driven decisions.