Abstract
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 article, 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; and 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.
Index Terms
- Extracting Refined Rules from Knowledge-Based Neural Networks
Recommendations
Dynamically adding symbolically meaningful nodes to knowledge-based neural networks
Traditional connectionist theory-refinement systems map the dependencies of a domain-specific rule base into a neural network, and then refine this network using neural learning techniques. Most of these systems, however, lack the ability to refine ...
Using Knowledge-Based Neural Networks to Improve Algorithms: Refining the Chou–Fasman Algorithm for Protein Folding
Special issue on multistrategy learningThis article describes a connectionist method for refining algorithms represented as generalized finite-state automata. The method translates the rule-like knowledge in an automaton into a corresponding artificial neural network, and then refines the ...
Extracting rules from neural networks for time series prediction
SoICT '14: Proceedings of the 5th Symposium on Information and Communication TechnologyA significant limitation of neural networks is that the representations they learn are usually incomprehensible to humans. There have been a number of research works that focused on how to extract rules from trained neural networks. Recently, Kamruzzaman ...
Comments