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EUQG

Uncertainty Measure In Evidence Theory

Di: Henry

Epistemic uncertainty is also called reducible, for it can be mitigated by acquiring more data or injecting prior knowledge into the problem. The theory of mathematical probability, developed by Andrey Kolmogorov [4], follows from classical measure theory and is the natural representation of aleatory uncertainty.

An improved distance-based total uncertainty measure in

Complex evidence theory, as a generation model of the Dempster-Shafer evidence theory, has the ability to express uncertainty and perform uncertainty reasoning. One of the key issues in complex evidence theory is the complex basic belief assignment (CBBA) generation method. But, how to model uncertainty information in complex evidence uncertainty is also called reducible theory is still an open Abstract Uncertainty measure in evidence theory supplies a new criterion to assess the quality and quantity of knowledge conveyed by belief structures. As generalizations of uncertainty measure in the probabilistic framework, several uncertainty measures for belief structures have been developed.

PPT - Uncertainty Quantification Using Evidence Theory With A Cost ...

Dempster–Shafer (DS) evidence theory is the most significant and effective method for uncertainty modeling and reasoning. How to measure the uncertainty in DS evidence theory precisely remains an outstanding problem. Various types of uncertainty measures for evidence have been presented. However, they all suffer some limitations. To address this Abstract Uncertainty measure in Dempster-Shafer (D-S) evidence theory is crucial to assess the quality of information conveyed by belief structures. Most of the previous studies consider this issue from the perspective of viewing the D-S evidence theory as

As an extension of probability theory, evidence theory is able to better handle unknown and imprecise information. Owing toits advantages, evidence theory has more flexibility and effectiveness for modeling and processing uncertain information.Uncertainty measure plays an essential role both in evidence theory and probability theory.In probability theory, Shannon Belief entropy is an effective uncertainty measurement in Dempster–Shafer evidence theory. However, the weight ratio between discord and non-specifici

This paper investigates the use of Evidence Theory to enhance the training efficiency of object detection models by incorporating uncertainty into the feedback loop. In each training iteration, during the validation phase, Evidence Theory is applied to establish a relationship between ground truth labels and predictions. The Dempster-Shafer rule of Abstract Complex evidence theory, as a generalized D-S evidence theory, has attracted academic attention because it can well express uncertainty by means of complex basic belief assignment (CBBA), and realize uncertainty reasoning by complex combination rule. However, the uncertainty measurement in complex evidence theory is still an open issue.

Deng entropy has some advantages and has been widely applied in many applications [44, 45, 46]. Other measures in the evidence theory include interval-value-based measure [47, 48], distance-based measures [49, 50] and so on [51]. Deng entropy will be utilized to deal with the uncertainty of risk assessments in FMEA in this work. Uncertainty quantification of mass functions is a crucial and unsolved issue in belief function theory. Previous studies have mostly considered this problem from the perspective of viewing the belief function theory as an extension of probability theory. Recently, Yang and Han have developed a new distance-based total uncertainty measure directly and totally based on

Plausibility Entropy: A New Total Uncertainty Measure in Evidence Theory Based on Plausibility Function Cui Y.; Deng X.

Uncertainty Quantification Using Evidence Theory

For two types of uncertainty involved in evidence theory, conflict, and nonspecificity, many measurement methods have been proposed on the basis of requirements of axiomatic conditions.

An improved measure for belief structure in the evidence theory [PeerJ]

An uncertainty measure SU for belief structures is then defined based on interval probabilities in the framework of evidence theory, without changing the theoretical frameworks.

Dempster–Shafer evidence theory (DS theory) has some superiorities in uncertain information processing for a large variety of applications. However, the problem of how to quantify the uncertainty of evidence theory include interval basic probability assignment (BPA) in DS theory Dempster–Shafer (D–S) evidence theory has been studied and applied broadly, owing to its advantage of effectively handling uncertainty problems in mul

Abstract Dempster Shafer (DS) theory, an extension of probability theory, is widely used for modeling uncertainty in information. It is based on the concept of basic probability assignment. Each basic probability value has a corresponding belief interval. These intervals offer a practical and understandable way to quantify uncertainty. However, none of the belief-interval Within complex evidence theory, the handling of CBBA uncertainty holds significant importance and remains an unresolved matter. Therefore, this paper introduces a novel approach, namely ambiguity-measure-based complex belief entropy Complex evidence theory (CET), as a generalized D-S evidence theory, has the ability to express uncertainty in the complex field. CET has been applied in many information fields. One of the key issues in CET is uncertainty measurement of the complex basic belief assignment (CBBA). Previous research on uncertainty measures usually focused on classical

Abstract Complex Evidence Theory (CET), an extension of the traditional D-S evidence theory, has garnered academic interest for its capacity to articulate uncertainty through Complex Basic Belief Assignment (CBBA) and to perform uncertainty reasoning using complex combination rules. The theory of belief functions is a very important and effective tool for uncertainty modeling and reasoning, where measures of uncertainty are very crucial for evaluating the degree of uncertainty in a body of evidence. Several uncertainty measures in the theory of belief functions have been proposed. 摘要： As an extension of probability theory,evidence theory is able to better handle unknown and imprecise information.Owing to its advantages,evidence theory has more flexibility and effectiveness for modeling and processing uncertain information.Uncertainty measure plays an essential role both in evidence theory and probability theory.In probability theory,Shannon

Complex Evidence Theory (CET), an extension of the traditional D-S evidence theory, has garnered academic interest for its capacity to articulate uncertainty through Complex Basic Belief Assignment (CBBA) and to perform uncertainty reasoning using complex combination rules. Nonetheless, quantifying uncertainty within CET remains a subject of ongoing research. The theory of evidence is a generalization of the probability theory which has been used in many applications. That generalization permits to represent more different types of uncertainty. To quantify the total information uncertainty in theory of evidence, several

A B S T R A C T We consider a wide range of measures of uncertainty that have been proposed within the Dempster–Shafer evidence theory. All these measures aim to quantify the uncertainty associated with a given basic probability assignment. As a preliminary step, we offer a study of the literature, practical and understandable way which shows a recent resurgence of interest in the quantification of uncertainty in the Dempster–Shafer (DS) evidence theory is the most significant and effective method for uncertainty modeling and reasoning. How to measure the uncertainty in DS evidence theory precisely remains an out

Traditional probability theory with strict separation of aleatory and epistemic uncertainty Treat epistemic uncertainty as possible realizations with no probability associated with those realizations obtained from sampling As an extension of probability theory,evidence theory is able to better handle unknown and imprecise information.Owing to its advantages,evidence theory has more flexibility and effectiveness for modeling and processing uncertain information.Uncertainty measure plays an essential role both in evidence theory and probability theory.In probability theory,Shannon

Uncertainty measure in evidence theory supplies a new criterion to assess the quality and quantity of knowledge conveyed by belief structures. As generalizations of uncertainty measure in the probabilistic framework， several uncertainty measures for belief structures have been developed. However， the inconsistency between evidential and probabilistic frameworks causes

AbstractDempster‐Shafer evidence theory has been widely used in many applications due to its advantages with weaker conditions than Bayes probability. How to measure the uncertainty of basic probab Request PDF | An improved failure mode and effects analysis method based on uncertainty measure in the evidence theory | mode and effects analysis (FMEA) is an effective tool to assess the risk of Evidence theory provides an effective representation and handling framework for uncertain information. However, the quantification for the uncertainty of mass function in this theory is still an unsolved problem. For two types of uncertainty involved in evidence theory, conflict, and nonspecificity, many measurement methods have been proposed on the basis of

However, how to handle the uncertainty in the subjective assessment is an open issue. In this paper, a novel method to deal with the uncertainty coming from subjective assessments of FMEA experts is proposed Measuring the uncertainty of basic probability assignment (BPA) is critical in applying DS evidence theory since it influences the accuracy of subsequent fusion and reasoning. Among the various uncertainty measures that have been proposed, Deng entropy has been widely recognized within the field as a significant indicator of uncertainty. Dempster–Shafer evidence theory has been widely used in various fields of applications, because of the flexibility and effectiveness in modeling uncertainties without prior information.