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TDBF: Two Dimensional Belief Function
Yangxue Li,Yong Deng +1 more
TL;DR: In this article, a two dimensional belief function (TDBF) is proposed to deal with uncertain information, which has two components, T=(mA,mB) and mB is a measure of reliability of the first component.
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Abstract: How to efficiently handle uncertain information is still an open issue. Inthis paper, a new method to deal with uncertain information, named as two dimensional belief function (TDBF), is presented. A TDBF has two components, T=(mA,mB). The first component, mA, is a classical belief function. The second component, mB, also is a classical belief function, but it is a measure of reliability of the first component. The definition of TDBF and the discounting algorithm are proposed. Compared with the classical discounting model, the proposed TDBF is more flexible and reasonable. Numerical examples are used to show the efficiency of the proposed method.
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Citations
Generalization of Dempster–Shafer theory: A complex mass function
TL;DR: A generalized Dempster–Shafer evidence theory is proposed, which provides a promising way to model and handle more uncertain information and an algorithm for decision-making is devised based on this theory.
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An association coefficient of a belief function and its application in a target recognition system
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TL;DR: The degree of association is defined by Deng Entropy, and a new association coefficient is proposed based on the basic inequality, which is applied to the target recognition system, and accurate results are obtained.
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Evidence combination using OWA-based soft likelihood functions
TL;DR: A novel evidence combination rule called CR‐SLF is proposed based on soft likelihood functions (SLF) considering the ordered weighted average aggregation operator, and two reliability‐based combination rules are presented, including the discount‐ based rule and the SLF improvement‐based rule.
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D-ANP: a multiple criteria decision making method for supplier selection
TL;DR: The D-ANP methodology is proposed to apply in the field of supplier selection, which is the extension of the traditional ANP method using D numbers, and the validity of the presented methodology is illustrated by an application for supplier selection.
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An interval‐valued exceedance method in MCDM with uncertain satisfactions
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TL;DR: An interval‐valued exceedance method is proposed to solve the question of how to reasonably obtain the aggregation results of alternatives based on the Golden Rule representative value and probabilistic exceedances method.
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