We present basic strategies for memory-restricted forgetting mechanisms of cases and propose a forgetting strategy which is a combination of the basic strategies. The effectivness of the proposed strategy for improving the performance of case-based reasoning systems is demonstrated through simulations in the electric power systems.

In this paper, we propose an algorithm of classification by feature partitioning (CFP) which learns concepts in the batch mode. The proposed algorithm achieved almost the same predictive accuracies as the best results of a CFP algorithm presented by Guvenir and Sirin. However, our algorithm is not affected by parameters and the order of examples.

In the domains involving environmental changes, some knowledge and heuristics which were useful for solving problems in the previous environment often become unsuitable for problems in the new environment. This paper describes two approaches to solve such problems in the context of case-based reasoning systems. The first one is maintaining descriptions of applicable scopes of cases through generalization and specialization. The generalization is performed to expand problem descriptions, i. e. descriptions of applicable scopes of cases. On the other hand, the specialization is performed to narrow problem descriptions of cases which failed to be applied to given problems with the aim of dealing with environmental changes. The second approach is forgetting, that is deleting obsolete cases from the case-base. However, the domain-dependent knowledge is necessary for testing obsolescence of cases and that causes the problem of knowledge acquisition. We adopt the strategies used by conventional learning systems and extend them using the least domain-dependent knowledge. These two approaches for adapting the case-base to the environment are evaluated through simulations in the domain of electric power systems.