Selecting representative cases by generalization capability

Case-base maintenance in Case-Based Reasoning (CBR) Systems is an important issue. If the case base is too large, it may include many redundant cases which will affect the solution accuracy. Therefore, removing redundant cases is a n important issue in CBR systems. In this paper, a new approach based on the Generalization Capability of cases to select the representative cases for Case-Base Maintenance is proposed. With this method, most redundant cases can be deleted and the most representative cases can be retained. The experiments show that the proposed method can greatly remove the redundant cases while preserving a satisfying degree of accuracy of solutions when applied to solve classification problems.