For decades, dempster shafer evidence theory provides a solution for information fusion with reduced uncertainty and ambiguity. Particularly appealing is the mathematical theory of evidence developed by arthur dempster. Abstract a modal logic interpretation of dempster shafer theory is developed in the framework of multivalued models of modal logic, i. The basic idea is to first model the link uncertainty by using dempster shafer theory in a unified framework, and then analyze the equilibria of games in terms of specific requirements. Section 3 describes algorithms for performing the most important operations on mass functions, and gives their efficiency.
On the decision making problem in dempstershafer theory. We challenge the validity of dempstershafer theory by using an emblematic example to show that ds rule produces counterintuitive result. Dempstershafer theory an overview sciencedirect topics. Ahmadzadeh and petrou used theory for predicting risk of soil erosion 11. Combination of evidence in dempster shafer theory kari sentz ph. Using a simple engineering example, i highlight sources of confusion in the dempster shafer literature, and some questions that arise in the course of applying the dempster shafer algorithm. The two modified mappings are utilized in two ambiguity measures in dempster shafer theory. Within the framework of dempster shafer theory, we propose a general model of partially reliable sources which includes several previously known results as special cases. The theory of belief functions, also referred to as evidence theory or dempstershafer theory dst, is a general framework for reasoning with uncertainty, with understood connections to other frameworks such as probability, possibility and imprecise probability theories. In this present paper, which is an extended version of 4, we aim at formally comparing this extension and dempster shafer theory. More precisely, we show that this extension can be reformulated in dempster shafer theory and thus, from an expressiveness point of view, it is equivalent to dempster shafer theory.
View enhanced pdf access article on wiley online library html view download pdf for offline viewing. Logistic regression, neural networks and dempstershafer theory. Oct 17, 2014 a python library for performing calculations in the dempster shafer theory of evidence. Indeed, probability theory may be seen as a limit of dempstershafer evidence theory when it is assumed. Student systems science and industrial engineering department thomas j. Dempster shafer theory is widely applied to uncertainty modelling and knowledge reasoning due to its ability of expressing uncertain information. In an earlier article 1 we derived a method, within the framework of dempster shafer theory 23, to handle evidences that are weakly speci. This paper discusses the relationship between dempster shafer theory and the classical bayesian method, describes our sensor fusion research work using dempster shafer theory in comparison with the weighted sum of probability method. Dec, 2019 the main goal of this paper is to describe an axiomatic utility theory for dempster shafer belief function lotteries. Download classic works of the dempstershafer theory of. Section 2 gives the mathematical fundamentals of dempster shafer theory.
Pdf download classic works of the dempster shafer theory. Most accurate prediction models require extensive testing e. Dempstershafer theory for combining in silico evidence. A python library for performing calculations in the dempster shafer theory of evidence. Shafer theory ds theory of belief functions for managing uncertainties, specifically in the auditing and information systems domains. This naturally leads us to choose the dempster shafer approach as our first sensor fusion implementation algorithm. Special issue on the dempstershafer theory of evidence. If youre looking for a free download links of classic works of the dempstershafer theory of belief functions studies in fuzziness and soft computing pdf, epub, docx and torrent then this site is not for you. The dempstershafer theory the dempstershafer theory 8 allows us to combine evidence from different experts or other sources and provides a measure of con. The dempstershafer ds theory of belief functions 1, 2 is now wellestablished as a formalism for reasoning. Download classic works of the dempster shafer theory of belief functions or read online books in pdf, epub, tuebl, and mobi format. Dempster in the context of statistical inference, the theory was later developed by glenn.
The dempster shafer ds theory is a powerful tool for probabilistic reasoning based on a formal calculus for combining evidence. Dempstershafer theory for combining in silico evidence and. The integrative risk analytic framework is grounded in the dempster shafer ds theory that allows the analyst to combine multiple pieces of evidence and judgments from independent sources of information. This paper presents a new classifier combination technique based on the dempster shafer theory of evidence. The ability to group complex data into a finite number of classes is. Dempstershafer theory and statistical inference with weak beliefs ryanmartin, jianchunzhangandchuanhailiu abstract. In 5, it explores the algorithmic point of view of the dempstershafer theory.
Handling of dependence in dempstershafer theory su. Unlimited viewing of the articlechapter pdf and any associated supplements and figures. However, the existing evidence theory is insufficient to consider the situations where it has no capability to express the fluctuations of data at a given phase of time during their execution, and the. Information fusion with dempstershafer evidence theory for. Jun 27, 2019 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. Pdf the dempstershafer theory of evidence researchgate. June 7, 2016 abstract the book that launched the dempster shafer theory. However, some conditions, such as exclusiveness hypothesis and completeness constraint, limit its development and application to a large extend. Section 5 investigates the semigroup structure of dempster shafer and states the main results relating it to bayes. Classic works of the dempstershafer theory of belief.
In this work we focus on the relationship between the dempster shafer. Nate blaylock, james allen, in plan, activity, and intent recognition, 2014. Dst originated when shafer extended the original work of dempster in 1976 10. The validity of dempstershafer belief functions core. However, it commonly has to suffer from the counterintuitive result.
Pdf algorithms for dempstershafer theory researchgate. Shafer showed how jeffreys generalization of bayes rule of conditioning can be reinterpreted in terms of the theory of belief functions. The dempster shafer theory of evidence jean gordon and edward h. Click download or read online button to get classic works of the dempster shafer theory of belief functions book now. Dempstershafer theory is based on the nonclassical idea of mass as opposed to the wellunderstood probabilities of bayes theory. A library written in java for performing calculations in the dempster shafer theory of evidence. A new failure mode and effects analysis model using dempster shafer evidence theory and grey relational projection method.
Dempstershafer evidence theory has been widely used in various fields of applications, because of the flexibility and effectiveness in. Dempster shafer theory dst 30 is a generalization of probability theory that allows for incomplete knowledge. A library written in java for performing calculations in the dempstershafer theory of evidence. The dempstershafer theory, also known as the theory of belief functions, is a generalization of the bayesian theory of subjective probability. Jeffreylike rules of conditioning for the dempstershafer theory of. Combination of evidence in dempstershafer theory kari sentz ph. Nguyen 1978 points out, soon after its introduction, t. The dempster shafer theory the dempster shafer theory 8 allows us to combine evidence from different experts or other sources and provides a measure of con. Download classic works of the dempster shafer theory of belief functions in pdf and epub formats for free. Software quality and reliability prediction using dempster. Combining testimonial reports from independent and partially reliable information sources is an important problem of uncertain reasoning. The main goal of this paper is to describe an axiomatic utility theory for dempster shafer belief function lotteries. Pdf section 2 gives the mathematical fundamentals of dempstershafer theory.
Shortliffe the drawbacks of pure probabilistic methods and of the certainty factor model have led us in recent years to consider alternate approaches. Shafer theory applied to regulatory decision process for selecting safer alternatives to toxic chemicals in consumer products. Whereas the bayesian theory requires probabilities for each question of interest, belief functions allow us to base degrees of belief for one question on probabilities for a. The dempstershafer theory owes its name to work by a. Dempstershafer theory, with an example given of fusion using the cornerstone of the theory known as dempsters rule. First, mass distributions in dempster shafer theory are obtained from the membership degrees of each pixel covering the three image components r, g and b. Classic works of the dempster shafer theory of belief. Engineering applications of artificial intelligence. The dempster shafer theory is a mathematical theory of evidence based on belief functions and plausible reasoning, which is used to combine separate pieces of information evidence to calculate the probability of an event.
Dempster shafer theory is a mathematical theory of evidence for the representation of uncertainty. Classic works of the dempstershafer theory of belief functions. An approach to the dempster shafer theory of evidence. Dempster shafer theory, with an example given of fusion using the cornerstone of the theory known as dempster s rule. Dempstershafer theory dst provides a way to take ignorance uncertainty due to reducible variability into account.
Pellegrini a athe mitre corporation, burlington road, bedford, ma 01730. Similarly to the ds theory, which is a generalisation of the classical probability theory, fuzzy logic provides an alternative reasoning apparatus as compared to boolean logic. Shafer theory applied to regulatory decision process. Dempster shafer evidence theory theory applied on several studies in computer science. This set is referred as the frame of discernment, and its. An introduction to bayesian and dempstershafer data fusion. Box 6000 binghamton, ny 9026000 scott ferson applied biomathematics 100 north country road setauket, ny 11733 abstract. Dempstershafer theory and statistical inference with. A mathematical theory of evidence turns 40 glenn shafer. With respect to polymatrix games with various link uncertainty, how do we deal with. Software quality and reliability prediction using dempstershafer theory lan guo dissertation submitted to the college of engineering and mineral resources at west virginia university in partial ful. Logistic regression, neural networks and dempstershafer. Since its introduction the very name causes confusion, a more general term often used is belief functions both used intermittently here.
Information fusion with dempstershafer evidence theory. Unlike the probabilistic case, our axiomatic framework leads to intervalvalued utilities, and therefore, to a partial incomplete. Watson school of engineering and applied science binghamton university p. This site is like a library, use search box in the widget to get ebook that you want. Data classification using the dempstershafer method arxiv. Shafer theory was presented in 22, although the proposed algorithm lacked a full formal justi. May 01, 2007 classic works of the dempster shafer theory of belief functions studies in fuzziness and soft computing yager, ronald r. Software quality and reliability prediction using dempstershafer theory lan guo as software systems are increasingly deployed in mission critical applications, accurate quality and reliability predictions are becoming a necessity. Dempster shafer theory dst is a rigorous decision theory approach that provides a way to generate predictions, estimate the uncertainty associated with each prediction, and combine multiple sources of evidence to obtain a weightofevidence prediction by quantitatively accounting for the reliability of each of the sources being combined. As a formal system, ds theory is distinguished from other uncertainty management approaches by dempster 1967, shafer 1976. First, mass distributions in dempstershafer theory are obtained from the membership degrees of each pixel covering the three image components r, g and b. This book brings together a collection of classic research papers on the dempstershafer theory of belief functions. A new failure mode and effects analysis model using. In this section brief information will be given to can comprehend combination technique.
The dempster shafer theory dst, also referred to as evidence theory, is a mathematical theory of evidence based on belief functions and plausible reasoning. Dempstershafer evidence theory for image segmentation. The dempstershafer theory of evidence jean gordon and edward h. The dempster shafer theory gives a solid ba sis for reasoning applications characterized by uncertainty. Shafer theory to combine the results obtained from individual sources to find a final unified ranking. Ds theory has been widely used in computer science and engi. When reasoning with such evidences we must avoid combining evidences by mistake that. The proposed method is composed essentially of two steps.
View dempster shafer theory research papers on academia. In particular, it provides explicit estimations of imprecision and con. The dempster shafer theory owes its name to work by a. A special feature of this theory is the possibility to characterize uncer. Pedersens model has been selected to estimate the probability of collision, using the information provided by the ais, and dempster shafer theory has been selected for the treatment of uncertainty. School of electronics and information technology, shanghai jiao tong university, shanghai 200240, peoples republic of china. Fault recognition using an ensemble classifier based on. Pdf remote sensing urban change detection based on. In the dempstershafer theory, this set is called a frame. Dempster s rule of combination can only be used when the. Dempster shafer theory and statistical inference with weak. This is a collection of classic research papers on the dempster shafer theory of belief functions.
The dempster shafer theory of evidence is a powerful method for combining measures of evidence from different classifiers. A key feature of the theory is that propositions are represented as subsets of a set. Dempstershafer theory, data classification, dempsters rule of combination. Then, similar to the mutual information in the probability theory, these measures are used for computing the dependency of the variables of a social bliss problem on the persons happiness value. Dempster shafer theory dst or evidence theory is a general framework for reasoning with uncertainty, the role of dst is to combine evidences from the rules r1and r2 and compute an overall belief.
The theory came to the attention of ai researchers in the early 1980s, when they were trying to adapt probability theory to expert systems. This methodology has been applied to maritime traffic in the canary islands and has been validated using the kullbackleibler divergence. In this paper, i discuss the basic notions of the dempster shafer theory. By bridging fuzzy logic and probabilistic reasoning, the theory of belief functions has become a primary tool for knowledge representation and uncertainty reasoning in expert systems. The initial work introducing dempster shafer ds theory is found in dempster 1967 and shafer 1976. The dempstershafer theory dst is a mathematical theory of evidence. A verified matlab toolbox for the dempstershafer theory.
Available evidence, even if incomplete, can be used to find imprecise probabilities for events. This research focuses on dempstershafers evidence theory and the different similarity measures used to compare two separate bodies of evidence boes. Classic works of the dempster shafer theory of belief functions book also available for read online, mobi, docx and mobile and kindle reading. Dempster shafer theory ds is an effective method to model uncertainties and combine multiple evidences. Expert systemsdempstershafer theory wikibooks, open books. An introduction liping liu college of business administration, university of akron, akron, oh 443254801 the notion of belief functions is a result of the seminar work of shafer1 and its conceptual forerunnerlower and upper probabilities. Download pdf classic works of the dempster shafer theory.
The dempster shafer ds theory of partial beliefs is one of the mostwell known formalisms to address the partial knowledge. The use of ds theory is illustrated by deriving a fraud risk assessment formula for a simplified version of a model developed by srivastava et al. Dempster 1968 and glenn shafer 1976, but the kind of reasoning the theory uses can be found as far back as the seventeenth century. This research focuses on dempstershafer s evidence theory and the different similarity measures used to. Support for normalized as well as unnormalized belief functions different montecarlo algorithms for combining belief functions various methods related to the generalized bayesian theorem measures of uncertainty methods for constructing belief functions from data both python 2. The dempstershafer theory, also known as the theory of belief functions, is a generalization of the. Author links open overlay panel luyuan chen yong deng. Dempster shafer theory is based on the nonclassical idea of mass as opposed to the wellunderstood probabilities of bayes theory. Xu and krzyzak applied theorem to handwriting recognition 9. The book is the authoritative reference in the field of evidential reasoning and an important archival reference in a wide range of areas including uncertainty reasoning in artificial intelligence and decision making in economics, engineering, and management. The following discussion within this section on dst has mostly been adopted from klir 11. Logistic regression, neural networks and dempster shafer theory.
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