Refining Expert Knowledge with an Artificial Neural Network.
Robert D. Andrews, Shlomo Geva · 1997
. This paper describes RULEIN/RULEX; an automated technique for the refinement of a knowledge base. RULEIN constructs a Rapid Backprop (RBP) network from an initial, partially complete/accurate knowledge base which is formulated as a set of propositional rules. The RBP network is then trained on a set of examples drawn from the problem domain. RULEX is then applied to the weights of the trained network to extract a set of `refined' propositional rules. The refined rule set represents the original knowledge base modified in the light of network training. Network training has the potential to remove inaccuracies in the original rule base, supplement partially correct initial rules, and add new rules. Rule initialisation can also speed up network learning by obviating the necessity of starting training from a tabula rasa configuration. RULEIN/RULEX is evaluated using rule `quality' criteria and results are presented for some benchmark problems. The method has application in many areas but...