TDCMF: Two-dimensional complex mass function with its application in decision-making. (October 2021)

• Record Type: Journal Article
• Authors: Guo, Hui; Xiao, Fuyuan
• Is Part Of: Engineering applications of artificial intelligence. Volume 105(2021)
• Publication Date: 2021-10
• Subjects: Uncertainty information -- Two-dimensional complex mass function -- Information fusion -- Decision-making -- Target identification -- Medical diagnosis; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2021.104409
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: How to efficiently handle uncertain information has always been a problem which puzzles some practitioners and researchers. The complex evidence theory has many applications in handling uncertain information. However, the complex evidence theory is incapable of considering the situation that when the system is becoming more and more complex. In this paper, therefore, a two-dimensional complex mass function (TDCMF) is proposed. The TDCMF are modeled as complex numbers and some basic concepts are newly defined. Moreover, the proposed TDCMF considers not only the support degree but also non-support degree and hesitation degree. Based on that, a combination rule of TDCMF is proposed. Then an algorithm for decision-making based on the proposed TDCMF and combination rule is proposed. Finally, two applications of the proposed algorithm are carried out to address the issue of target identification and medical diagnosis in a comparative way, which validates the rationality of the proposed algorithm.