Evidence-Based Uncertainty Modeling

2015 ◽  
pp. 105-124
Author(s):  
Tazid Ali
Keyword(s):  
Evidence Theory ◽  
Uncertainty Modeling ◽  
Possibility Theory ◽  
Dempster Shafer Theory ◽  
Uncertainty Measurement ◽  
Frame Work ◽  
Shafer Theory ◽  
Relationship Of ◽  
Different Sources ◽  
Fuzzy Setting

Evidence is the essence of any decision making process. However in any situation the evidences that we come across are usually not complete. Absence of complete evidence results in uncertainty, and uncertainty leads to belief. The framework of Dempster-Shafer theory which is based on the notion of belief is overviewed in this chapter. Methods of combining different sources of evidences are surveyed. Relationship of probability theory and possibility theory to evidence theory is exhibited. Extension of the classical Dempster-Shafer Structure to fuzzy setting is discussed. Finally uncertainty measurement in the frame work of Dempster-Shafer structure is dealt with.

Evidence-Based Uncertainty Modeling

2014 ◽  
pp. 54-74
Author(s):  
Tazid Ali
Keyword(s):  
Evidence Theory ◽  
Uncertainty Modeling ◽  
Possibility Theory ◽  
Dempster Shafer Theory ◽  
Uncertainty Measurement ◽  
Frame Work ◽  
Shafer Theory ◽  
Relationship Of ◽  
Different Sources ◽  
Fuzzy Setting

Evidence is the essence of any decision making process. However in any situation the evidences that we come across are usually not complete. Absence of complete evidence results in uncertainty, and uncertainty leads to belief. The framework of Dempster-Shafer theory which is based on the notion of belief is overviewed in this chapter. Methods of combining different sources of evidences are surveyed. Relationship of probability theory and possibility theory to evidence theory is exhibited. Extension of the classical Dempster-Shafer Structure to fuzzy setting is discussed. Finally uncertainty measurement in the frame work of Dempster-Shafer structure is dealt with.

scholarly journals Measuring the Uncertainty in the Original and Negation of Evidence Using Belief Entropy for Conflict Data Fusion

Entropy ◽  
2021 ◽  
Vol 23 (4) ◽  
pp. 402
Author(s):  
Yutong Chen ◽  
Yongchuan Tang
Keyword(s):  
Information Processing ◽  
Data Fusion ◽  
Evidence Theory ◽  
Uncertain Information ◽  
Dempster Shafer Theory ◽  
Open World ◽  
Uncertainty Measurement ◽  
Basic Probability ◽  
Shafer Theory ◽  
Deng Entropy

Dempster-Shafer (DS) evidence theory is widely used in various fields of uncertain information processing, but it may produce counterintuitive results when dealing with conflicting data. Therefore, this paper proposes a new data fusion method which combines the Deng entropy and the negation of basic probability assignment (BPA). In this method, the uncertain degree in the original BPA and the negation of BPA are considered simultaneously. The degree of uncertainty of BPA and negation of BPA is measured by the Deng entropy, and the two uncertain measurement results are integrated as the final uncertainty degree of the evidence. This new method can not only deal with the data fusion of conflicting evidence, but it can also obtain more uncertain information through the negation of BPA, which is of great help to improve the accuracy of information processing and to reduce the loss of information. We apply it to numerical examples and fault diagnosis experiments to verify the effectiveness and superiority of the method. In addition, some open issues existing in current work, such as the limitations of the Dempster–Shafer theory (DST) under the open world assumption and the necessary properties of uncertainty measurement methods, are also discussed in this paper.

scholarly journals Evidence Theory in Picture Fuzzy Set Environment

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Harish Garg ◽  
R. Sujatha ◽  
D. Nagarajan ◽  
J. Kavikumar ◽  
Jeonghwan Gwak
Keyword(s):  
Fuzzy Sets ◽  
Fuzzy Set ◽  
Evidence Theory ◽  
Belief Function ◽  
Uncertain Information ◽  
Interval Probability ◽  
Dempster Shafer Theory ◽  
Effective Manner ◽  
Picture Fuzzy Set ◽  
Shafer Theory

Picture fuzzy set is the most widely used tool to handle the uncertainty with the account of three membership degrees, namely, positive, negative, and neutral such that their sum is bound up to 1. It is the generalization of the existing intuitionistic fuzzy and fuzzy sets. This paper studies the interval probability problems of the picture fuzzy sets and their belief structure. The belief function is a vital tool to represent the uncertain information in a more effective manner. On the other hand, the Dempster–Shafer theory (DST) is used to combine the independent sources of evidence with the low conflict. Keeping the advantages of these, in the present paper, we present the concept of the evidence theory for the picture fuzzy set environment using DST. Under this, we define the concept of interval probability distribution and discuss its properties. Finally, an illustrative example related to the decision-making process is employed to illustrate the application of the presented work.

scholarly journals Probability Estimation in the Framework of Intuitionistic Fuzzy Evidence Theory

2015 ◽  
Vol 2015 ◽  
pp. 1-10
Author(s):  
Yafei Song ◽  
Xiaodan Wang
Keyword(s):  
Evidence Theory ◽  
Estimation Method ◽  
Probability Estimation ◽  
Belief Functions ◽  
Intuitionistic Fuzzy ◽  
Dempster Shafer Theory ◽  
Fuzzy Environment ◽  
Shafer Theory ◽  
Theory Of Evidence ◽  
Interval Valued

Intuitionistic fuzzy (IF) evidence theory, as an extension of Dempster-Shafer theory of evidence to the intuitionistic fuzzy environment, is exploited to process imprecise and vague information. Since its inception, much interest has been concentrated on IF evidence theory. Many works on the belief functions in IF information systems have appeared. Although belief functions on the IF sets can deal with uncertainty and vagueness well, it is not convenient for decision making. This paper addresses the issue of probability estimation in the framework of IF evidence theory with the hope of making rational decision. Background knowledge about evidence theory, fuzzy set, and IF set is firstly reviewed, followed by introduction of IF evidence theory. Axiomatic properties of probability distribution are then proposed to assist our interpretation. Finally, probability estimations based on fuzzy and IF belief functions together with their proofs are presented. It is verified that the probability estimation method based on IF belief functions is also potentially applicable to classical evidence theory and fuzzy evidence theory. Moreover, IF belief functions can be combined in a convenient way once they are transformed to interval-valued possibilities.

scholarly journals Multimodel Approach Based on Evidence Theory for Forecasting Tropical Cyclone Tracks

2010 ◽  
Vol 138 (2) ◽  
pp. 405-420 ◽  
Author(s):  
Svetlana V. Poroseva ◽  
Nathan Lay ◽  
M. Yousuff Hussaini
Keyword(s):  
Tropical Cyclone ◽  
Evidence Theory ◽  
Dempster Shafer Theory ◽  
South Indian ◽  
Cyclone Tracks ◽  
Environmental Prediction ◽  
Shafer Theory ◽  
Theory Of Evidence ◽  
Year 2000 ◽  
Tropical Cyclone Tracks

Abstract In this paper a new multimodel approach for forecasting tropical cyclone tracks is presented. The approach is based on the Dempster–Shafer theory of evidence. At each forecast period, the multimodel forecast is given as an area where the tropical cyclone position is likely to occur. Each area includes a quantitative assessment of the credibility (degree of belief) of the prediction. The multimodel forecast is obtained by combining individual model forecasts into a single prediction by Dempster’s rule. Mathematical requirements associated with the Dempster’s rule are discussed. Particular attention is given to the requirement of independence of evidence sources, which, for tropical cyclone track forecasting, are the model and best-track data. The origin of this requirement is explored, and it is shown that for forecasting tropical cyclone tracks, this requirement is excessive. The influence of the number of models included in the multimodel approach on the forecasting ability is also studied. Data produced by the models of the Navy Operational Global Atmospheric Prediction System, the European Centre for Medium-Range Weather Forecasts, and the National Centers for Environmental Prediction are used to produce two-, three-, and four-model forecasts. The forecasting ability of the multimodel approach is evaluated using the best-track database of the tropical cyclones that occurred in the eastern and western North Pacific and South Indian Ocean basins in the year 2000.

Multi-Node Spectrum Sensing Based on a Novel Dempster-Shafer Theory

2011 ◽  
Vol 128-129 ◽  
pp. 625-628
Author(s):  
Zhen Dong Yin ◽  
Shan He
Keyword(s):  
Spectrum Sensing ◽  
Information Fusion ◽  
Signal To Noise Ratio ◽  
Evidence Theory ◽  
Application Range ◽  
Threshold Detection ◽  
Dempster Shafer Theory ◽  
Shafer Theory ◽  
Clear Line ◽  
Double Threshold

In this paper, a novel and efficient Dempster-Shafer (D-S) evidence theory multi-node spectrum sensing based on double threshold judgment is proposed. A specific coordinate operation of D-S theory and double threshold judgment is discussed. It defines the uncertain area in double threshold detection, controls the application range of D-S theory and obtains the final detection results by drawing a clear line between data decision and information fusion. A better performance and higher sensing efficiency in a low signal-to-noise ratio is resulted according to the simulations.

scholarly journals Pipe burst diagnostics using evidence theory

2010 ◽  
Vol 13 (4) ◽  
pp. 596-608 ◽  
Author(s):  
Josef Bicik ◽  
Zoran Kapelan ◽  
Christos Makropoulos ◽  
Dragan A. Savić
Keyword(s):  
Distribution System ◽  
Water Distribution ◽  
Information Sources ◽  
Water Distribution System ◽  
Evidence Theory ◽  
Dempster Shafer Theory ◽  
Pipe Burst ◽  
The Individual ◽  
Shafer Theory ◽  
Theory Of Evidence

This paper presents a decision support methodology aimed at assisting Water Distribution System (WDS) operators in the timely location of pipe bursts. This will enable them to react more systematically and promptly. The information gathered from various data sources to help locate where a pipe burst might have occurred is frequently conflicting and imperfect. The methodology developed in this paper deals effectively with such information sources. The raw data collected in the field is first processed by means of several models, namely the pipe burst prediction model, the hydraulic model and the customer contacts model. The Dempster–Shafer Theory of Evidence is then used to combine the outputs of these models with the aim of increasing the certainty of determining the location of a pipe burst within a WDS. This new methodology has been applied to several semi-real case studies. The results obtained demonstrate that the method shows potential for locating the area of a pipe burst by capturing the varying credibility of the individual models based on their historical performance.

scholarly journals Translating Islamic identity into numbers

2021 ◽  
Vol 5 (S1) ◽  
pp. 139-159
Author(s):  
Andino Maseleno ◽  
Miftachul Huda ◽  
Mazdi Marzuki ◽  
Fauziah Che Leh ◽  
Azmil Hashim ◽  
...  
Keyword(s):  
Evidence Theory ◽  
Theory Model ◽  
Ordinal Scale ◽  
Average Level ◽  
Dempster Shafer Theory ◽  
Islamic Identity ◽  
Specific Individual ◽  
Shafer Theory ◽  
Theory Of Evidence

This research aims to define various Islamic based identity profile to different individuals by identifying the various degrees of Islamic based identity profile. A scale of measurement in ordinal scale has been used to determine an Islamic based identity profile. The scale is subdivided into three main subsections, namely very rarely, average level and very frequently. By using the scale of measurement on an ordinal scale, it assists in developing a numerical hypothesis that is then used to determine an individual's Islamic based identity profile using the Dempster-Shafer theory of evidence. Using twenty-four set of questions, the research used the evidence presented to support a given Islamic based identity profile of a specific individual and filtered it using various degrees of probabilities of the evidence theory model, which have aided in proving or validating a particular hypothesis. The questions are divided into three types  based on Islamic identity profile which include Fitrah, Khalifah and Din. The conclusion made is that we may be able to easily diagnose an individual’s Islamic based identity profile using Dempster-Shafer theory of evidence.

scholarly journals Temporal and Causal Relations on Evidence Theory: an Application on Adverse Drug Reactions

2021 ◽  
Vol 34 (1) ◽  
Author(s):  
Luiz Alberto Pereira Afonso Ribeiro ◽  
Ana Cristina Bicharra Garcia ◽  
Paulo Sérgio Medeiros Dos Santos
Keyword(s):  
Information Fusion ◽  
Evidence Theory ◽  
Mass Function ◽  
Data Sources ◽  
Drug Reactions ◽  
Clinical Notes ◽  
Dempster Shafer Theory ◽  
Multisensor Information Fusion ◽  
Shafer Theory ◽  
Theory Of Evidence

The use of big data and information fusion in electronichealth records (EHR) allowed the identification of adversedrug reactions(ADR) through the integration of heteroge-neous sources such as clinical notes (CN), medication pre-scriptions, and pathological examinations. This heterogene-ity of data sources entails the need to address redundancy,conflict, and uncertainty caused by the high dimensionalitypresent in EHR. The use of multisensor information fusion(MSIF) presents an ideal scenario to deal with uncertainty,especially when adding resources of the theory of evidence,also called Dempster–Shafer Theory (DST). In that scenariothere is a challenge which is to specify the attribution of be-lief through the mass function, from the datasets, named basicprobability assignment (BPA). The objective of the presentwork is to create a form of BPA generation using analy-sis of data regarding causal and time relationships betweensources, entities and sensors, not only through correlation, butby causal inference.

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