Abstract
Uncertainty measurement of the basic probability assignment function has always been a hot issue in Dempster-Shafer evidence. Many existing studies mainly consider the influence of the mass function itself and the size of the frame of discernment, so that the correlation between the subsets is ignored in the power set of the frame of discernment. Without making full use of the information contained in the evidence, the existing methods are less effective in some cases given in the paper. In this paper, inspired by Shannon entropy and Deng entropy, we propose an improved entropy that not only inherits the many advantages of Shannon entropy and Deng entropy, but also fully considers the relationship between subsets, which makes the improved entropy overcome the shortcomings of existing methods and have greater advantages in uncertainty measurement. Many numerical examples are used to demonstrate the validity and superiority of our proposed entropy in this paper.
Highlights
Uncertainty exists in all aspects of life, and it has attracted the attention and research of many scholars [1]–[6]
PRELIMINARIES we introduce some preliminaries that needs to be used in our proposed method, which include D-S evidence theory, Shannon entropy, Deng entropy and so on
Through Equation 13, we can find that the belief entropy we proposed is similar to Deng entropy and Zhou et al entropy
Summary
Uncertainty exists in all aspects of life, and it has attracted the attention and research of many scholars [1]–[6]. There are many uncertain measurement methods that have been proposed to measure the uncertainty of D-S evidence theory, but the results are not satisfactory because they can’t effectively measure the uncertainty of BPA [9], [54], [56], [59], [66]–[69]. It was not until Deng proposed Deng entropy that this problem was solved initially [70]. Summarize the contributions of the proposed entropy in this paper
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
