An information-based approach to handle various types of uncertainty in fuzzy bodies of evidence

Abstract

Fuzzy evidence theory, or fuzzy Dempster-Shafer Theory captures all three types of uncertainty, i.e. fuzziness, non-specificity, and conflict, which are usually contained in a piece of information within one framework. Therefore, it is known as one of the most promising approaches for practical applications. Quantifying the difference between two fuzzy bodies of evidence becomes important when this framework is used in applications. This work is motivated by the fact that while dissimilarity measures have been surveyed in the fields of evidence theory and fuzzy set theory, no comprehensive survey is yet available for fuzzy evidence theory. We proposed a modification to a set of the most discriminative dissimilarity measures (smDDM)-as the minimum set of dissimilarity with the maximal power of discrimination in evidence theory-to handle all types of uncertainty in fuzzy evidence theory. The generalized smDDM (FsmDDM) together with the one previously introduced as fuzzy measures make up a set of measures that is comprehensive enough to collectively address all aspects of information conveyed by the fuzzy bodies of evidence. Experimental results are presented to validate the method and to show the efficiency of the proposed method.

Weighted assignment fusion algorithm of evidence conflict based on Euclidean distance and weighting strategy, and application in the wind turbine system

“…To deal with the diversity, uncertainty, and conflict of information, researchers have proposed ideas of feature correlation, difference, different conflict values, and non-similarity measures. They improved and integrated algorithms [27][28][29][30][31] from mathematical perspectives, such as mean, combination, and entropy. Zhang et al [32] proposed a method incorporating fuzzy object elements, Monte Carlo simulation, and DST, through weighted averaging and data deblurring rules, the result has clear analytical values to represent the final risk level.…”

Research on the Design of a Hexagonal Shaft Straightening Machine Based on Quality Function Development and Evidence Theory

“…The use of evidence theory to integrate expert opinions and describe the uncertainty factors in the analysis process can effectively improve the accuracy of expert opinion assessment in the degree of correlation and reduce the components of conflict. Sarabi-Jamab et al [14] proposed a modification to a set of the most discriminative dissimilarity measures (smDDM) as the minimum set of dissimilarity with the maximal power of discrimination in evidence theory to handle all types of uncertainty in fuzzy evidence theory. The generalized smDDM (FsmDDM) together with the one previously introduced as fuzzy measures make up a set of measures that is comprehensive enough to collectively address all aspects of information conveyed by the fuzzy bodies of evidence.…”

“…Monte Carlo simulation is used along with the two theories to propagate the uncertainty. Sarabi-Jamab et al [14] put forward a modification to a set of the most discriminative dissimilarity measures (smDDM), combined with the generalized smDDM (FsmDDM), a set of comprehensive measurement dimensions is constructed. The method can deal with all three types of uncertainty: fuzziness, non-specificity, and conflict, and in practical application, the effectiveness of the proposed method is proved by quantifying the differences between fuzzy bodies of evidence.…”