Dynamic maintenance case base using knowledge discovery techniques for case based reasoning systems

Abstract

The achievement of a Case Based Reasoning (CBR) system is strongly related to the quality of case data and the rapidity of the retrieval process that depends on the quantity of the cases. This quality can diminish especially when the number of cases gets outsized. To guarantee this quality, maintenance the case base becomes essentially. Much existing maintenance CBR approaches focus on the performance of the CBR or the study of the case base (CB) competence. Even though the two points are directly related, there is a few research on using strategies at both points at the same time. Furthermore, the proposed methods are not dynamic, they are not suitable for the frequently change in learning process. In this paper, we propose maintenance CBR method based on well-organized machine learning techniques, in the process of improving the competence and the performance of the CB and can handle incremental cases which evolve over time. We support our approach with empirical evaluation using different benchmark data sets to show the effectiveness of our method.