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, P. R. China
Abstract: Case retrieval is a major step in case-based reasoning (CBR), which seeks the most similar historical case to correspond to the target case. However, the first step of similarity measurement is to determine the weights of attributes, which would affect the accuracy of the similarity calculation results. In this study, we propose a new method, called DEA-CBR that integrates the double frontiers data envelopment analysis (DEA) to determine the most similar historical case based on the similarity efficiency of each historical case. This proposed method is different from the traditional distance-based similarity measurement methods in that attribute weights are determined by DEA models without the need to be specified. The proposed DEA-CBR approach first defines attribute distances between each historical case and target case to calculate attribute similarity for each attribute. The maximum and the minimum similarity efficiencies of each historical case are then measured with DEA models and are geometrically averaged to measure the overall similarity efficiency of each historical case, based on which the most similar historical case can be determined. Two numerical examples are provided to illustrate the potential applications and benefits of the proposed DEA-CBR method.
