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.

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.

EVIDENCE THEORY BASED ON GENERAL CONSEQUENCE RELATIONS

1995 ◽  
Vol 06 (02) ◽  
pp. 119-135 ◽  
Author(s):  
PHILIPPE BESNARD ◽  
JÜRG KOHLAS
Keyword(s):  
Infinite Order ◽  
Evidence Theory ◽  
Consequence Relations ◽  
Support Functions ◽  
Consequence Relation ◽  
Dempster Shafer Theory ◽  
Duality Relations ◽  
Shafer Theory ◽  
Theory Of Evidence ◽  
Usual Duality

The Dempster-Shafer theory of evidence can be conceived as a theory of probability of provability. In fact, it has been shown that evidence theory can be developed on the basis of assumption-based reasoning. Taking this approach, reasoning is modeled in this paper by a consequence relation in the sense of Tarski. It is shown that it is possible to construct evidence theory on top of the very general logics defined by these consequence relations. Support functions can be derived which are, as usual, set functions, monotone of infinite order. Furthermore, plausibility functions can also be defined. However, as negation need not be defined in these general logics, the usual duality relations between support and plausibility functions of Dempster-Shafer theory do not hold in general. Nonetheless, this symmetry can be installed progressively by considering logics that enjoy more and more “structural properties”.

A New Multisensor Information Fusion Model Using Dempster-Shafer Theory

2013 ◽  
Vol 475-476 ◽  
pp. 415-418
Author(s):  
Jian Li ◽  
Ying Wang ◽  
Zhi Jie Mao
Keyword(s):  
Information Fusion ◽  
Fusion Process ◽  
Contextual Knowledge ◽  
Fusion Model ◽  
Primary Mass ◽  
Model Based ◽  
Dempster Shafer Theory ◽  
Multisensor Information Fusion ◽  
Shafer Theory

The aim of this paper is to investigate how to use the contextual knowledge in order to improve the fusion process. The concept of multisensor information fusion model based on the Dempster-Shafer Theory is introduced. The resulting information of the architecture is combined using similar sensor subset and dissimilar sensor subset. We demonstrate the effectiveness of this approach using the uncertain and disparate information compared to primary mass assignment techniques.

scholarly journals A MODIFIED D NUMBERS METHODOLOGY FOR ENVIRONMENTAL IMPACT ASSESSMENT

2017 ◽  
Vol 24 (2) ◽  
pp. 653-669 ◽  
Author(s):  
Ningkui WANG ◽  
Daijun WEI
Keyword(s):  
Environmental Impact ◽  
Impact Assessment ◽  
Environmental Impact Assessment ◽  
Previous Method ◽  
Combination Process ◽  
Dempster Shafer Theory ◽  
Shafer Theory ◽  
Theory Of Evidence ◽  
D Numbers ◽  
Commutative Property

Environmental impact assessment (EIA) is usually evaluated by many factors influenced by various kinds of uncertainty or fuzziness. As a result, the key issues of EIA problem are to rep­resent and deal with the uncertain or fuzzy information. D numbers theory, as the extension of Dempster-Shafer theory of evidence, is a desirable tool that can express uncertainty and fuzziness, both complete and incomplete, quantitative or qualitative. However, some shortcomings do exist in D numbers combination process, the commutative property is not well considered when multiple D numbers are combined. Though some attempts have made to solve this problem, the previous method is not appropriate and convenience as more information about the given evaluations rep­resented by D numbers are needed. In this paper, a data-driven D numbers combination rule is proposed, commutative property is well considered in the proposed method. In the combination process, there does not require any new information except the original D numbers. An illustrative example is provided to demonstrate the effectiveness of the method.

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