TY - JOUR
T1 - On the negation of a Dempster–Shafer belief structure based on maximum uncertainty allocation
AU - Deng, Xinyang
AU - Jiang, Wen
N1 - Publisher Copyright:
© 2019 Elsevier Inc.
PY - 2020/4
Y1 - 2020/4
N2 - Probability theory and Dempster–Shafer theory are two germane theories to represent and handle uncertain information. Recent study suggested a transformation to obtain the negation of a probability distribution based on the maximum entropy. Correspondingly, determining the negation of a belief structure, however, is still an open issue in Dempster–Shafer theory, which is very important in theoretical research and practical applications. In this paper, a negation transformation for belief structures is proposed based on maximum uncertainty allocation, and several important properties satisfied by the transformation have been studied. The proposed negation transformation is more general and could be totally compatible with existing transformation for probability distributions.
AB - Probability theory and Dempster–Shafer theory are two germane theories to represent and handle uncertain information. Recent study suggested a transformation to obtain the negation of a probability distribution based on the maximum entropy. Correspondingly, determining the negation of a belief structure, however, is still an open issue in Dempster–Shafer theory, which is very important in theoretical research and practical applications. In this paper, a negation transformation for belief structures is proposed based on maximum uncertainty allocation, and several important properties satisfied by the transformation have been studied. The proposed negation transformation is more general and could be totally compatible with existing transformation for probability distributions.
KW - Belief structure
KW - Dempter–Shafer theory
KW - Maximum uncertainty
KW - Negation transformation
KW - Uncertainty modelling
UR - http://www.scopus.com/inward/record.url?scp=85077319580&partnerID=8YFLogxK
U2 - 10.1016/j.ins.2019.12.080
DO - 10.1016/j.ins.2019.12.080
M3 - 文章
AN - SCOPUS:85077319580
SN - 0020-0255
VL - 516
SP - 346
EP - 352
JO - Information Sciences
JF - Information Sciences
ER -