In Case-Based Reasoning (CBR), new problems are solved by retrieving similar previously solved cases and adapting their solutions. The new case is then stored appropriately in the case-base for future use. It is a fundamental problem to control the growth of case-base and the case-base maintenance step retains cases in the case-base based on an estimate of their usefulness in solving new problems. We propose an optimization formulation to identify an optimal set of representative cases called the optimal footprint of the case-base. The optimization formulation ensures that the optimal footprint set strikes a right trade-off between minimizing the number of cases and maximizing their ability to solve the remaining cases in the case-base. This trade-off is studied empirically in this paper. We also illustrate the trade-off between the size and performance of optimal footprint in the context of regression.

In case-based reasoning (CBR), problems are solved by retrieving prior cases and adapting their solutions to fit; learning occurs as new cases are stored. Controlling the growth of the case base is a fundamental problem, and research on case-base maintenance has developed methods for compacting case bases while maintaining system competence, primarily by competence-based deletion strategies assuming static case adaptation knowledge. This paper proposes adaptation-guided case-base maintenance (AGCBM), a case-base maintenance approach exploiting the ability to dynamically generate new adaptation knowledge from cases. In AGCBM, case retention decisions are based both on cases' value as base cases for solving problems and on their value for generating new adaptation rules. he paper illustrates the method for numerical prediction tasks (case-based regression) in which adaptation rules are generated automatically using the case difference heuristic. In comparisons of AGCBM to five alternative methods in four domains, for varying case base densities, AGCBM outperformed the alternatives in all domains, with greatest benefit at high compression.