Generating Fuzzy Rules For Case - based Classification

As a technique to solve new problems based on previous successful cases, CBR represents significant prospects for improving the accuracy and effectiveness of unstructured decision-making problems. Similar problems have similar solutions is the main assumption. Utility oriented similarity modeling is gradually becoming an important direction for Case-based reasoning research. In this thesis, we propose a new way to represent the utility of case by using fuzzy rules. Our method could be considered as a new way to estimate case utility based on fuzzy rule based reasoning. We use modified WANG's algorithm to generate a fuzzy if-then rule from a case pair instead of a single case. The fuzzy if-then rules have been identified as a powerful means to capture domain information for case utility approximation than traditional similarity measures based on feature weighting. The reason why we choose the WANG algorithm as the foundation is that it is a simpler and faster algorithm to generate if-then rules from examples. The generated fuzzy rules are utilized as a case matching mechanism to estimate the utility of the cases for a given problem. The given problem will be formed with each case in the case library into pairs which are treated as the inputs of fuzzy rules to determine whether or to which extent a known case is useful to the problem. One case has an estimated utility score to the given problem to help our system to make decision. The experiments on several data sets have showed the superiority of our method over traditional schemes, as well as the feasibility of learning fuzzy if-then rules from a small number of cases while still having good performances. PREFACE First and foremost, we really appreciate our advisor, Ning Xiong, for his supervision on our thesis. His suggestions and comments have contributed greatly to the completion of this thesis. His advice for improving the innovation to our thesis and coordinating independence and teamwork on finishing a work are really helpful to us. We are also very grateful for the scholarship and the exchange project between MDH and ECUST (East China University of Science and Technology) which provides us the opportunity to study in Sweden. At the same time we do believe that after finishing the thesis, we learn a lot from each other. To do the thesis together is an unforgettable experience for both of us.