Improving the Reliability of Case-Based Reasoning Systems

Case-based reasoning (CBR) infers a solution to a new problem by searching a collection of previously solved problems for cases which are similar to the new problem. The collection of previous problems and their associated solutions represents the CBR system's realm of expertise. A CBR system helps to exploit data so that smarter decisions can be made in less time and/or at lower cost. A key issue is that can we always trust the solutions suggested by a case-based reasoning system? This paper studies the reliability of CBR systems based on previous study results, factors affecting the reliability of a CBR system are also discussed in this paper, especially the property that whether inter-feature of case exists redundancy. After that, the reliability of an individual suggested solution is studied. To illustrate these ideas, some experiments and their results are discussed in this paper. The results of experiments show a new route concerning on how to improve the reliability of a CBR system at an overall level.