Rule learning by searching on adapted nets

Abstract

If the back propagation network can produce an inference structure with high and robust performance, then it is sensible to extract rules from it. The KT algonthm is a novel algonthm for generating rules from an adapted net efficiently. The algorithm is able to deal with both single-layer and multi-layer networks, and can learn both confirming and disconfirming rules. Empirically, the algorithm is demonstrated in the domain of wind shear detection by infrared sensors with success.