Combining Symbolic and Connectionist Learning Methods to Refine Certainty-Factor Rule-Bases (1996)
This research describes the system RAPTURE, which is designed to revise rule bases expressed in certainty-factor format. Recent studies have shown that learning is facilitated when biased with domain-specific expertise, and have also shown that many real-world domains require some form of probabilistic or uncertain reasoning in order to successfully represent target concepts. RAPTURE was designed to take advantage of both of these results.

Beginning with a set of certainty-factor rules, along with accurately-labelled training examples, RAPTURE makes use of both symbolic and connectionist learning techniques for revising the rules, in order that they correctly classify all of the training examples. A modified version of backpropagation is used to adjust the certainty factors of the rules, ID3's information-gain heuristic is used to add new rules, and the Upstart algorithm is used to create new hidden terms in the rule base.

Results on refining four real-world rule bases are presented that demonstrate the effectiveness of this combined approach. Two of these rule bases were designed to identify particular areas in strands of DNA, one is for identifying infectious diseases, and the fourth attempts to diagnose soybean diseases. The results of RAPTURE are compared with those of backpropagation, C4.5, KBANN, and other learning systems. RAPTURE generally produces sets of rules that are more accurate that these other systems, often creating smaller sets of rules and using less training time.

PhD Thesis, Department of Computer Sciences, University of Texas at Austin. 113 pages.

Jeff Mahoney Ph.D. Alumni mahoney [at] firstadvisors com