A TFN-based uncertainty modeling method in complex evidence theory for decision making

Complex evidence theory, as a generation model of the Dempster-Shafer evidence theory, has the ability to express uncertainty and perform uncertainty reasoning. One of the key issues in complex evidence theory is the complex basic belief assignment (CBBA) genera-tion method. But, how to model uncertainty information in complex evidence theory is still an open issue. In this paper, therefore, we propose a CBBA generation method by taking advantage of the triangular fuzzy number. Moreover, an algorithm for decision making is devised based on the proposed CBBA generation method. Finally, the decision making algo-rithm is applied in classification to verify its effectiveness. In summary, the proposed method can handle uncertainty modeling and reasoning both in the real number domain and the complex number domain, which provides a promising way in decision making theory. (c) 2022 Elsevier Inc. All rights reserved.

Automated summary

Complex evidence theory, as a generation model of the Dempster-Shafer evidence theory, can express and reason uncertainty. The complex basic belief assignment (CBBA) generation method is a key issue in this theory, and modeling uncertainty information remains an open issue. In this paper, a CBBA generation method utilizing triangular fuzzy numbers is proposed, and a decision-making algorithm based on this method is developed. The effectiveness of the algorithm is verified through its application in classification. Overall, the proposed method offers a promising approach for uncertainty modeling and reasoning in both the real number domain and the complex number domain in decision making theory.