A case of combination of evidence in the dempstershafer theory inconsistent with evaluation of probabilities. Pdf a new combination rule for conflict problem of. 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 related question. Watson school of engineering and applied science binghamton university p. A weighted combination method for conflicting evidence in. In particular, when considered together with the ds rule of combination, it is not always clear when mass can be made consistent with the standard probability evaluation. Combination of evidence in dempstershafer theory citeseerx. Combination of evidence in dempstershafer theory core. It is shown that application of the dempstershafer rule of combination in this case leads to an evaluation of masses of the combined. Modified dempstershafer ds evidence theory can handle.
Dempstershafer evidence theory and study of some key. Student systems science and industrial engineering department thomas j. Shafer theory of evidence to combine optimally results obtained by content. A botnet is an army of compromised computers controlled by a bot herder and used for illicit financial gain. An algorithm for updating the evidence in the dempstershafer theory is presented. This paper reports on a largescale experiment for the evaluation of a formal querybiased combination of evidence mechanism. However, formatting rules can vary widely between applications and fields of interest or study. Pdf combination of evidence in dempstershafer theory. Ge school of surveying and spatial information systems, the university of new south wales, sydney nsw 2052, australia ph. Its strict probabilistic assumptions are rarely fulfilled in real applications and there are even no rare examples where the results of the dempsters rule are counterintuitive. Dempstershafer theory for a querybiased combination of. The algorithm allows to carry out the reasoning with updating the evidence in much more ef.
Combination of type iii digit recognizers using the. A key feature of the dempstershafer theory is the rule for combining bodies of evidence. Dempstershafer theory for a querybiased combination of evidence. Shortliffe the drawbacks of pure probabilistic methods and of the certainty factor model have led us in recent years to consider alternate approaches. If is the basic probability assignment provided by source, the combination. This report surveys a number of possible combination rules for dempstershafer structures and provides examples of the implementation of these rules for discrete and intervalvalued data. Dempsters rule of combination can only be used when the bodies of evidence are assumed to be independent. Dempstershafer evidence theory and study of some key problems. Abstractas one of the most important mathematical methods, the dempstershafer ds evidence theory has been widely used in date fusion, risk assessment, target. Dempstershafer 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 weightof evidence prediction by quantitatively accounting for the reliability of each of the sources being combined. Shafer theory to combine the results obtained from individual sources to find a final unified ranking.
This approach allows combination of sensor outputs that effectively classify different. An important aspect of this theory is the combination of evidence obtained from multiple sources and the modeling of conflict between them. Box 6000 binghamton, ny 9026000 scott ferson applied biomathematics 100 north country road setauket, ny 11733 abstract. Evidence combination can be interpreted, in some applications, as classifier fusion. On the dempstershafer framework and new combination rules. As a formal system, ds theory is distinguished from other uncertainty management approaches by dempster 1967, shafer 1976.
Citeseerx combination of evidence in dempstershafer theory. Handling of dependence in dempstershafer theory su. These indices are used to code the process of reasoning under uncertainty the combination of evidence using the dempstershafer theory. Combination of evidence in dempstershafer theory book. The dempstershafer mathematical theory of evidence has attracted considerable attention as a promising method of dealing with some of the basic problems arising in combination of evidence and data fusion. Honeypots are network surveillance architectures designed to resemble easytocompromise computer systems. To deal with such a problem, we put forward a new method based on the distance of evidence. The dempstershafer theory, also known as the theory of belief functions, is a generalization of the bayesian theory of subjective probability. Dempstershafer theory does not require an assumption.
Sandia is a multiprogram laboratory operated by sandia corporation, keyphrases. Dempstershafer theory for combining in silico evidence. The implicit handling of uncertainty and ambiguous classes in this approach are particularly useful in applications of multimodal sensor fusion. Conflicting evidence combination based on uncertainty. Conjunctive and disjunctive combination of belief functions. Pdf the dempstershafer theory of evidence researchgate. The significant innovation of this framework is that it allows for the allocation of a probability mass to sets or intervals. Citeseerx document details isaac councill, lee giles, pradeep teregowda. Exploring the combination of dempstershafer theory and. They are deployed to trap hackers in order to help security professionals capture, control, and analyze malicious internet attacks and other activities of hackers. The sources of evidence corresponding to multiple classifiers usually exhibit different classification qualities, and they are often discounted. Dempsters rule, and the requirement that the items of evidence combined be.
The dempstershafer theory of evidence jean gordon and edward h. Combination of evidence in dempstershafer theory 2002 cached. Combination of evidence in dempster shafer theory sandia. This report surveys a number of possible combination rules for dempstershafer structures and provides examples of the implementation of these rules. The theory of belief functions has an established nexus with probability theory, possibility theory, and, by extension, fuzzy set theory. Dempstershafer theory provides the ability to deal with ignorance and missing information.
Updating the evidence is necessary among others in systems. Conflict management is an open issue in dempstershafer evidence theory. However, the counterintuitive results may be obtained when combining with highly conflicting evidence. Generalizations of probability theory 2 dempstershafer belief functions 3 combining the evidence. Since then, nearly four h undred pap ers on the theory and practice of ds reasoning has b een published in ieee journals and conference. Pdf updating the evidence in the dempstershafer theory. Tossing a coin which is known to be fair tossing a coin which is not known to be fair. Paradox elimination in dempstershafer combination rule with. Pdf dempstershafer theory offers an alternative to traditional probabilistic theory for the mathematical representation of uncertainty.
Particularly appealing is the mathematical theory of evidence. In this paper a special case of combination of two bodies of evidence with nonzero conflict coefficient is considered. Combination of evidence in dempstershafer theory kari sentz ph. Evidence theory, also called belief function theory, provides an efficient tool to represent and combine uncertain information for pattern classification.
This paper proposes a systematic approach to handle dependence in evidence theory. The dempstershafer theory of evidence accumulation is one of the main tools for combining data obtained from multiple sources. Evidence theory, dempstershafer theory, transferable belief model. Combination of type iii digit recognizers using the dempstershafer theory of evidence catalin i tomai and sargur s. We use the dempstershafer theory of evidence to combine optimally results obtained by content and link analyses on the web. In this paper, we propose a new theory called generalized evidence theory get. Shafer combination creates a desired synergy between 2 bodies of knowledge, which improves the precision of the top. A new combination rule for conflict problem of dempstershafer evidence theory.
Evidence combination dempstershafer theory provides a method to combine the previous measures of evidence of different sources. Dempstershafer evidence theory is widely applied in various fields related to information fusion. Dempstershafer theory an overview sciencedirect topics. The ds theory offers provision for the representation of ignorance.
Dempstershafer evidence theory is widely applied in. In this paper, we study predicting trust and distrust based on dempstershafer theory and neural network. Therefore, exploring the combination of dempstershafer theory and neural network can potentially improve the performance and bring new opportunities for trust and distrust prediction. Dempstershafer evidence theory is widely used in many fields of information fusion. Central to dempstershafer theory is the rule of combination of beliefs. The algorithm allows to carry out the reasoning with updating the evidence in much more efficient way than using the original. The dempstershafer theory dst is a mathematical theory of evidence.
A case of combination of evidence in the dempstershafer. 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 past decades, a large amount of research has been conducted on this issue. It sho wn that application of the dempstershafer rule com bination in this case leads to ev aluation of masses. Dempstershafer evidence theory is an efficient tool in knowledge reasoning and decisionmaking under uncertain environments. This paper presents a new classifier combination technique based on the dempstershafer theory of evidence. A new combination rule in evidence theory qianhui dong, qian sun, fei yu abstractmultitarget tracking system is used to distinguish the class from different targets and obtain the trajectory of all targets from multiple information systems. Dempstershafer theory offers an alternative to traditional probabilistic theory for the mathematical representation of uncertainty. In terms of of amending dempsters combination rule, the major works.
Evidence combination from an evolutionary game theory perspective xinyang deng, deqiang han, jean dezert, yong deng, and yu shyr abstract dempstershafer evidence theory is a primary methodology for multisource information fusion since it allows to deal with uncertain information. Combination of evidence in dempstershafer theory unt. Demonstrate the use of the dempstershafer theory of evidence in a tactical military intelligence decision aid. For combination of beliefs the dempsters rule of combination is used in the dempstershafer theory. Evidence combination based on credal belief redistribution. The querybiased mechanism is based on the query scope, a. The dempstershafer theory of evidence is a powerful method for combining measures of evidence from different classifiers. Dempstershafer evidence combining for antihoneypot.
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