Comparing Methods For Refining Certainty Factor Rule-Bases (1994)
This paper compares two methods for refining uncertain knowledge bases using propositional certainty-factor rules. The first method, implemented in the RAPTURE system, employs neural-network training to refine the certainties of existing rules but uses a symbolic technique to add new rules. The second method, based on the one used in the KBANN system, initially adds a complete set of potential new rules with very low certainty and allows neural-network training to filter and adjust these rules. Experimental results indicate that the former method results in significantly faster training and produces much simpler refined rule bases with slightly greater accuracy.
In Proceedings of the Eleventh International Workshop on Machine Learning (ML-94), pp. 173--180, Rutgers, NJ, July 1994.

Jeff Mahoney Ph.D. Alumni mahoney [at] firstadvisors com
Raymond J. Mooney Faculty mooney [at] cs utexas edu