Constructive Induction in Knowledge-Based Neural Networks

Artificial neural networks have proven to be a successful, general method for inductive learning from examples. However, they have not often been viewed in terms of constructive induction. We describe a method for using a knowledgebased neural network of the kind created by the KBANN algorithm as the basis of a system for constructive induction. After training, we extract two types of rules from a network: modified versions of the rules initially provided to the knowledgebased neural network, and rules which describe newly constructed features. Our experiments show that the extracted rules are more accurate, at classifying novel examples, than the trained network from which the rules are extracted.