Memory and forgetting: An improved dynamic maintenance method for case-based reasoning

The problem solving performance of a case-based reasoning (CBR) system is closely related to the quantity and quality of the cases stored in the case base. With the continuous growth of the size of the case base, the so called “swamping problem” may occur when the time cost of retrieval exceeds the benefit of the accuracy. From the perspective of cognitive science, a dynamic maintenance method improved by selective memory and intentional forgetting for CBR is proposed, which can imitate the memory function of the human brain to selectively save new cases, update the forgotten values and intentionally delete the old cases. The experiments show the effectiveness of the proposed method. The selective memory and international forgetting policy can significantly reduce the time and space complexity, and retain or improve the accuracy of the CBR classifier, thus improving the performance of CBR.