Maintaining case-based expert systems using fuzzy neural network

Abstract

There is always a trade off between the number of cases to be stored in the case library of a case-based expert system and the performance of retrieval efficiency. The larger the case library, the more the problem space covered, however, it would also downgrade the system performance if the number of cases grows to an unacceptably high level. In the paper, an approach to maintaining the size of a case-based expert system is proposed. The main idea is using the fuzzy class membership value of each record, determined by a trained neural network, to guide the record deletion. These fuzzy membership values are used to calculate the case density of each record, and a deletion policy can then be used to determine the percentage of record to be deleted. Using this approach, we could maintain the size of the case-base without loosing a significant amount of information. A testing case-base consisting of 214 records is used as an illustrative example of our approach, the neural network software NEURALWORKS PROFESSIONAL II/PLUS/sup (C)/ is used to develop the neural network. It was shown that it could reduce the size of the case library by 28% if we select those records that have an overall class membership of over 0.8 and case density over 0.95. Future work includes integrating adaptation rules for building deletion policy.