Regret Theory-Based Case-Retrieval Method with Multiple Heterogeneous Attributes and Incomplete Weight Information

Case retrieval is a crucial step in case-based reasoning (CBR), which is related to decision-making effectiveness. To improve decision support, CBR usually calculates case similarity and evaluates utility. However, the psychological behavior of decision makers is seldom considered in case retrieval. This paper proposes a novel case-retrieval method that deals with multiple heterogeneous attributes and incomplete weight information based on regret theory (RT). First, we define the function of the perceived utility based on attribute similarity and RT. Next, a mathematical programming model is constructed to determine the attribute weights based on linear programming technique for multidimensional analysis of preference (LINMAP). Based on this, we can calculate the perceived utility and determine a set of similar historical cases. Furthermore, the utilities of the evaluated attributes are calculated based on RT and LINMAP. Subsequently, we compute the comprehensive utilities of similar historical cases and obtain the ranking order of similar historical cases. Thus, the most suitable historical case is obtained. Finally, a case study of a gas explosion is conducted to illustrate the use of the proposed method. (C) 2021 The Authors. Published by Atlantis Press B.V.