An approach to case-based maintenance: Selecting representative cases

Case-based maintenance is an important issue in Case-Based Reasoning (CBR) System. Generally speaking, the larger the case-base, the more accurate the solution. However, if the case base is too large, it may include many redundant cases and the case retrieve will not be effective. Moreover redundant cases will affect the solution accuracy. Therefore, removing redundant cases is a fundamental issue in maintaining CBR systems. In this paper, a new approach based on the Generalization Capability of cases to select the representative cases for Case-Based Maintenance is proposed. Using this method, most redundant cases can be deleted and the most representative cases can be identified and retained. The experiments show that the proposed method can greatly remove the redundant cases as well as preserve a satisfying degree of accuracy of solutions when it is used for classification tasks.