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.

中文翻译：

---

基于复区间数的不确定性建模方法及其在决策融合中的应用

复杂证据理论是 Dempster-Shafer 证据理论的推广，是一种有效的复值域决策融合不确定性推理。特别地，复杂基本信念分配（CBBA）的生成是复杂证据理论中不确定性建模的关键问题。在本文中，我们首先构建复区间数（CIN）模型。在这种情况下，我们提出了一种新的 CBBA 生成方法来模拟复杂平面框架中的不确定性。此外，我们在基于 CIN 的 CBBA 生成方法的基础上提出了一种新的决策算法。通过在几个真实世界数据集上的模式识别应用，验证了所提出的决策算法的效率。