Complex interval number‐based uncertainty modeling method with its application in decision fusion

Abstract

Complex evidence theory, a generalization of Dempster–Shafer evidence theory, is an effective uncertainty reasoning for decision fusion in complex‐valued domain. In particular, the generation of complex basic belief assignment (CBBA) is a key issue for uncertainty modeling in complex evidence theory. In this paper, we first construct complex interval number (CIN) model. In this context, we propose a novel CBBA generation method to model uncertainty in the framework of complex planes. Furthermore, we propose a novel decision‐making algorithm on the basis of the CIN‐based CBBA generation method. Through an application in pattern recognition on several real‐world data sets, the efficiency of the proposed decision‐making algorithm is verified.