A total uncertainty measure for D numbers based on belief intervals
This addresses an unsolved issue in uncertainty reasoning for D numbers, but it is incremental as it builds on existing distance-based measures from Dempster-Shafer theory.
The paper tackles the problem of measuring uncertainty in D numbers, a generalization of Dempster-Shafer theory, by proposing a total uncertainty measure based on belief intervals that captures discord, non-specificity, and non-exclusiveness, with properties like range and monotonicity presented.
As a generalization of Dempster-Shafer theory, the theory of D numbers is a new theoretical framework for uncertainty reasoning. Measuring the uncertainty of knowledge or information represented by D numbers is an unsolved issue in that theory. In this paper, inspired by distance based uncertainty measures for Dempster-Shafer theory, a total uncertainty measure for a D number is proposed based on its belief intervals. The proposed total uncertainty measure can simultaneously capture the discord, and non-specificity, and non-exclusiveness involved in D numbers. And some basic properties of this total uncertainty measure, including range, monotonicity, generalized set consistency, are also presented.