A fuzzy-rough approach for the maintenance of distributed case-based reasoning systems

Case-based reasoning (CBR) means reasoning from prior examples and it has considerable potential for building intelligent assistant system for the World Wide Web. In order to develop successful Web-based CBR systems, we need to select a set of representative cases for the client side case-base such that this thin client is competence in problem solving. This paper proposes a fuzzy-rough method of selecting cases for such a distributed CBR system, i.e., a thin client system (a smaller case-base with rules) connected to a comparatively more powerful server system (the entire original case-base). The methodology is mainly based on the idea that an original case-base can be transformed into a smaller case-base together with a group of fuzzy adaptation rules, which could be generated using our fuzzy-rough approach. As a result, the smaller case-base with a group of fuzzy rules will almost have the same problem coverage as the entire original case-base. The method proposed in this paper, consists of four steps. First of all, an approach of learning feature weights automatically is used to evaluate the importance of different features in a given case-base. Secondly, clustering of cases is carried out to identify different concepts in the case-base using the acquired feature weights. Thirdly, fuzzy adaptation rules are mined for each concept using a fuzzy-rough method. Finally, a selection strategy which based on the concepts of case coverage and reachability is used to select representative cases. The effectiveness of our method is demonstrated experimentally using some testing data in the travel domain.