Affiliations: [a]
College of Electronics and Information Science, Fujian Jiangxia University, Fujian, P. R. China | [b]
Decision Sciences Institute, Fuzhou University, Fujian, P. R. China | [c]
Department of Information Engineering, Fujian Chuanzheng Communications College, Fuzhou, PR China
Abstract: Case retrieval is the major step in case-based reasoning (CBR). The similarity measurement between historical cases and the target case is very important in the case retrieval, and affects the results of the decision. In CBR practical application, there are hybrid attribute values for case attributes. The representation of the case and performing case retrieval with high retrieval accuracy for hybrid multiple formats of attribute values are significant challenges, but an in-depth study is lacking. The objective of this paper is to develop a new case retrieval method to hybrid multi-attribute, which contains four formats of attribute values, i.e., crisp numbers, interval numbers, multi-granularity linguistic variables, and intuitionistic fuzzy numbers (IFNs). First, crisp numbers, interval numbers, and multi-granularity linguistic variables are transformed into IFNs and an attribute similarity measurement based on IFNs is proposed. The attribute weights are determined by an optimal matching model. This model belongs to a type of multi-objective problem and can be solved using the min-max method. Furthermore, the case similarities between historical cases and the target case are obtained by aggregating attribute similarities using evidence reasoning, and the proper historical case(s) can be retrieved according to the obtained hybrid case similarities. Finally, a case study of the gas explosion in China’s Fujian province is conducted to demonstrate the proposed approach and its potential application.
Keywords: Case retrieval, intuitionistic fuzzy number, evidence reasoning, similarity measurement, gas explosion
