An approach utilizing negation of extended-dimensional vector of disposing mass for ordinal evidences combination in a fuzzy environment
This work addresses uncertainty measurement in fuzzy environments for researchers in evidence theory, but appears incremental as it builds on existing methods by adding ordinal considerations.
The paper tackles the problem of measuring uncertainty in a frame of discernment by introducing an ordinal frame of discernment to account for proposition sequences, and proposes a method combining proposition order and mass using computer vision concepts to better manifest relationships, with a vector-level indicator for uncertainty.
How to measure the degree of uncertainty of a given frame of discernment has been a hot topic for years. A lot of meaningful works have provided some effective methods to measure the degree properly. However, a crucial factor, sequence of propositions, is missing in the definition of traditional frame of discernment. In this paper, a detailed definition of ordinal frame of discernment has been provided. Besides, an innovative method utilizing a concept of computer vision to combine the order of propositions and the mass of them is proposed to better manifest relationships between the two important element of the frame of discernment. More than that, a specially designed method covering some powerful tools in indicating the degree of uncertainty of a traditional frame of discernment is also offered to give an indicator of level of uncertainty of an ordinal frame of discernment on the level of vector.