Abstract
Evidence theory is widely used in information fusion. However, how to combine highly conflicting evidence is still an open issue. In this paper, a modified average method is proposed to address this issue based on the belief entropy and induced ordered weighted averaging operator. One of the advantages of the proposed method is that both the uncertainty and reliability of evidence are considered. In addition, it provides a right for the decision maker to combine the evidence based on the requirements for the precision of the results. A numerical example is shown to illustrate the use of the proposed method and an application based on sensor fusion in fault diagnosis is given to demonstrate the efficiency of our proposed method.
Highlights
In recent years, how to deal with uncertain information has been paid much attention [1]–[3]
Evidence theory as one of the most effective tools of handling uncertain information is widely used in many fields, such as fault diagnosis [4]–[6], data fusion [7]–[9], evidential reasoning [10], [11], pattern classification [12] and so on
Xiao: Combine Conflicting Evidence Based on the Belief Entropy and induced ordered weighted averaging (IOWA) Operator averaging (OWA) operator [30], in which the preference relationship of the decision maker is taken into account
Summary
How to deal with uncertain information has been paid much attention [1]–[3]. Xiao: Combine Conflicting Evidence Based on the Belief Entropy and IOWA Operator averaging (OWA) operator [30], in which the preference relationship of the decision maker is taken into account. An evidence combination method is proposed on the purpose of combining highly conflicting evidence. Both the importance of uncertainty and reliability of the evidence are taken into consideration to modify the evidence source. The result that the patient has a brain tumor with probability 1.0 is clearly counterintuitive because both of the two doctors are both agree that it is highly unlikely that P has a brain tumor
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