G. Towell & J. Shavlik (1993).
The Extraction of Refined Rules from Knowledge-Based Neural Networks. Machine Learning, 13, pp. 71-101.
This publication is available remotely and available in postscript.
Neural networks, despite their empirically-proven abilities, have been little used for the refinement of existing knowledge because this task requires a three-step process. First, knowledge must be inserted into a neural network. Second, the network must be refined. Third, the refined knowledge must be extracted from the network. We have previously described a method for the first step of this process. Standard neural learning techniques can accomplish the second step. In this paper, we propose and empirically evaluate a method for the final, and possibly most difficult, step. Our method efficiently extracts symbolic rules from trained neural networks. The four major results of empirical tests of this method are that the extracted rules:
(1) closely reproduce the accuracy of the network from which they are extracted;
(2) are superior to the rules produced by methods that directly refine symbolic rules;
(3) are superior to those produced by previous techniques for extracting rules from trained neural networks;
(4) are ``human comprehensible.''
Thus, this method demonstrates that neural networks can be used to effectively refine symbolic knowledge. Moreover, the rule-extraction technique developed herein contributes to the understanding of how symbolic and connectionist approaches to artificial intelligence can be profitably integrated.
Computer Sciences Department
College of Letters and Science
University of Wisconsin - Madison
INFORMATION ~ PEOPLE ~ GRADS ~ UNDERGRADS ~ RESEARCH ~ RESOURCES
5355a Computer Sciences and Statistics ~ 1210 West Dayton Street, Madison, WI 53706
email@example.com ~ voice: 608-262-1204 ~ fax: 608-262-9777