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A total uncertainty measure for D numbers based on belief intervals
Author(s) -
Deng Xinyang,
Jiang Wen
Publication year - 2019
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.22195
Subject(s) - dempster–shafer theory , measure (data warehouse) , consistency (knowledge bases) , generalization , monotonic function , measurement uncertainty , uncertainty theory , mathematics , belief structure , sensitivity analysis , uncertainty quantification , uncertainty analysis , range (aeronautics) , computer science , set (abstract data type) , artificial intelligence , data mining , mathematical optimization , statistics , mathematical analysis , materials science , composite material , programming language
Uncertainty quantification is very important in many applications. 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 nonspecificity, and nonexclusiveness involved in D numbers. And some basic properties of this total uncertainty measure, including range, monotonicity, generalized set consistency, are also presented. At last, an illustrative application about feature evaluation is given to verify the effectiveness of the proposed uncertainty measure.