Abstract—Case-Based Reasoning (CBR) is one of machine learning algorithms for problem solving and learning that caught a lot of attention over the last few years. In general, CBR is composed of four main phases: retrieve the most similar case or cases, reuse the case to solve the problem, revise or adapt the proposed solution, and retain the learned cases before returning them to the case base for learning purpose. Unfortunately, in many cases, this retain process causes the uncontrolled case base growth. The problem affects competence and performance of CBR systems. This paper proposes competence-based maintenance method based on deletion policy strategy for CBR. There are three main steps in this method. Step 1, formulate problems. Step 2, determine coverage and reachability set based on coverage value. Step 3, reduce case base size. The results obtained show that this proposed method performs better than the existing methods currently discussed in literature.

The success of a Case Based Reasoning (CBR) system depends on the quality of case data and the speed of the retrieval process that can be expensive in time especially when the number of cases gets large. To guarantee this quality, maintenance the contents of a case base becomes necessarily. As a result, the research area of Case Base Maintenance (CBM) has drawn more and more attention to CBR systems. This paper provides a snapshot of the state of the art, reviewing some important methods of maintaining case based reasoning. We introduce a framework for distinguishing these methods and compare and analyze them. In addition, this paper also presents simulations on data sets from U.C.I repository to show the effectiveness of some CBM methods taking into account the accuracy, the size and the retrieval time of case bases. Our simulation results which are obtained by compared well known reduction techniques show that these CBM methods have good storage reduction ratios, satisfying classification accuracies and short retrieval time.

We have developed a diagnostic help system dedicated to the maintenance of a supervised industrial system for pallets Transfer (SISTRE). This diagnostic help system is based on a Case-Based Reasoning approach (CBR). The expertise considered in this help system and formalized in the case form in a case-base must be updated, while taking account of its quality. In this objective we propose a method allowing on one hand to structure the case-base and on the other hand to auto-increment it. An experimental study is undertaken through references benchmarks as well as an application on SISTRE.