Logical method for logical operations based on evidential reasoning
International Journal of Knowledge Engineering and Soft Data Paradigms
MDAI '09 Proceedings of the 6th International Conference on Modeling Decisions for Artificial Intelligence
Experimental study of uncertainty measures with sensor fusion techniques
CAR'10 Proceedings of the 2nd international Asia conference on Informatics in control, automation and robotics - Volume 2
Generalized information theory based on the theory of hints
ECSQARU'11 Proceedings of the 11th European conference on Symbolic and quantitative approaches to reasoning with uncertainty
Generalized information theory for hints
International Journal of Approximate Reasoning
A Model for Decision Making with Missing, Imprecise, and Uncertain Evaluations of Multiple Criteria
International Journal of Intelligent Systems
Information-based dissimilarity assessment in Dempster-Shafer theory
Knowledge-Based Systems
A belief classification rule for imprecise data
Applied Intelligence
Hi-index | 0.00 |
In the framework of evidence theory, ambiguity is a general term proposed by Klir and Yuan in 1995 to gather the two types of uncertainty coexisting in this theory: discord and nonspecificity. Respecting the five requirements of total measures of uncertainty in the evidence theory, different ways have been proposed to quantify the total uncertainty, i.e., the ambiguity of a belief function. Among them is a measure of aggregate uncertainty, called AU, that captures in an aggregate fashion both types of uncertainty. But some shortcomings of AU have been identified, which are that: 1) it is complicated to compute; 2) it is highly insensitive to changes in evidence; and 3) it hides the distinction between the two types of uncertainty that coexist in every theory of imprecise probabilities. To overcome the shortcomings, Klir and Smith defined the TU1 measure that is a linear combination of the AU measure and the nonspecificity measure N. But the TU1 measure cannot solve the problem of computing complexity, and brings a new problem with the choice of the linear parameter delta. In this paper, an alternative measure to AU for quantifying ambiguity of belief functions is proposed. This measure, called Ambiguity Measure (AM), besides satisfying all the requirements for general measures also overcomes some of the shortcomings of the AU measure. Indeed, AM overcomes the limitations of AU by: 1) minimizing complexity for minimum number of focal points; 2) allowing for sensitivity changes in evidence; and 3) better distinguishing discord and nonspecificity. Moreover, AM is a special case of TU1 that does not need the parameter delta