Discovery of Rules and Generalization by Structural Learning of Neural Networks

Abstract

The discovery of explicit rules by back propagation learning of neural networks is extremely hard due to the difficulty in interpreting hidden units of resulting networks. This paper proposes that the structural learning with forgetting can discover rules in the form of Boolean functions. Database on mushrooms is used to demonstrate that the discovery of Boolean functions classifying mushrooms into edible or poisonous is possible. The comparative study of the generalization abilities of back propagation learning, the structural learning with forgetting and ID3 in artificial intelligence is also presented.