The dempstershafer theory is a natural, important and useful extension of classical probability theory. Updating the evidence in the dempstershafer theory deepdyve. Dempstershafer theory does not require an assumption regarding the probability of the individual constituents of the set or interval. A case of combination of evidence in the dempstershafer theory. It sho wn that application of the dempstershafer rule com bination in this case leads to ev aluation of masses.
On the dempstershafer framework and new combination rules. In this paper a special case of combination of two bodies of evidence with nonzero conflict coefficient is considered. The initial work introducing dempstershafer ds theory is found in dempster 1967 and shafer 1976. Handling of dependence in dempstershafer theory su 2015. The limitations of the original ds combination rule and works to eliminate them are discussed in section 4. Dempstershafer theory, data classification, dempsters rule of combination. Get addresses conflict management in an open world, where the. A simple view of the dempstershafer theory of evidence and. Section 5 investigates the semigroup structure of dempstershafer and states the main results relating it to bayes. Special issue on the dempstershafer theory of evidence. 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. Ds is a mathematical theory of evidence based on belief functions and plausible. Apr 01, 2002 an important aspect of this theory is the combination of evidence obtained from multiple sources and the modeling of conflict between them.
However, when the evidence is severely conflicting, the result could be counterintuitive. Gordon and others published the dempstershafer theory of evidence find, read and cite all the research you. Combination of evidence in dempstershafer theory core. There is no good practice that can be generally accepted until the presence of generalized evidence theory get. Paradox elimination in dempstershafer combination rule. A simple view of the dempstershafer theory of evidence and its. Dempstershafer theory offers an alternative to traditional probabilistic theory for the mathematical representation of uncertainty. This theory may be considered as a generalization of the probability theory. The dempstershafer theory of evidence is a powerful method for combining measures of evidence from different classifiers. Application of dempster shafer theory to the in silico prediction of chemical toxicity based on qsar models and rulebased structural alerts demonstrates the advantages of this approach. There is, however, a simple way of approach ing the dempster shafer theory that only requires a min. Pdf the dempstershafer theory of evidence researchgate. The application of dempstershafer theory demonstrated.
It is shown that application of the dempstershafer rule of combination in this case leads to an evaluation of masses of the combined bodies that is. Timedomain data fusion using weighted evidence and dempster. A new technique for combining multiple classifiers using. A case of combination of evidence in the dempstershafer. An algorithm for updating the evidence in the dempstershafer theory is presented. Particularly appealing is the mathematical theory of evidence developed by arthur dempster. It sho wn that application of the dempstershafer rule com bination. Jeffreylike rules of conditioning for the dempstershafer theory of. An important aspect of this theory is the combination of evidence obtained from.
This site is like a library, use search box in the widget to get ebook that you want. The 8step algorithm is applied to timedomain to achieve the sequential combination of timedomain data. Dempstershafer theory dst, also known as the theory of evidence or the theory of belief functions, was introduced by shafer in 1976 1 based on dempsters previous works 2, 3, 4. A flexible rule for evidential combination in dempster. Shafer combination creates a desired synergy between 2 bodies of knowledge, which improves the precision of the top. In this context the dempster shafer theory of evidence dst has shown some promising results. As a more flexible mathematical tool, dempstershafer theory not only combines with other mathematical frameworks 1518, but also combines with. A simple view of the dempstershafer theory of evidence and its implication for the rule of combination lotfi a. Zadeh compufer science division, universify of california, berkeley, california 94720 the emergence of expert systems as one of the major ar. Shafer theory to combine the results obtained from individual sources to find a final unified ranking. The dempstershafer theory of evidence jean gordon and edward h.
Dempsters combination rule in dempstershafer theory of evidence is widely used to combine multiple pieces of evidence. This is a potentially valuable tool for the evaluation of risk and reliability in engineering applications when it is not possible to obtain a precise measurement from experiments, or when knowledge is obtained. Pdf combination of evidence in dempstershafer theory. As a tool to manipulate an uncertain environment, ds evidence theory established a rounded system for uncertainty management and information fusion 3,4,5,6. A mathematical theory of evidence download ebook pdf. The paper presents an algorithm for translating the subontologies containing con. This paper presents a new classifier combination technique based on the dempster shafer theory of evidence. Conflict management is an open issue in dempstershafer evidence theory. Seims this thesis is an assessment on the effectiveness of dempstershafers theory of evidence in comparison to classical probabilistic combination as it applies to synthetic aperture radar sar automatic target recognition atr. A study of dempstershafers theory of evidence in comparison. Dempstershafer theory, introduced and developed by dempster and shafer 6,7,8, has many merits by contrast to bayesian probability theory. Sentiment prediction based on dempstershafer theory of evidence.
The application of dempstershafer theory demonstrated with justi. The application of dempstershafer theory demonstrated with. Dempstershafer ds theory 1 is an effective tool to make a decision from several answers with ambiguity. To apply data fusion in timedomain based on dempstershafer ds combination rule, an 8step algorithm with novel entropy function is proposed. In this section, we first present a brief description of dempstershafer ds theory and then describe the way in which we apply it to the score aggregation problem. A method of multiclassifier combination based on dempster. Combining multiple query interface matchers using dempster. The dempstershafer theory of evidence is a formalism, which allows appropriate interpretation of extractors con. The dempster shafer theory of evidence jean gordon and edward h. A problem is aroused in multiclassifier system that normally each of the classifiers is considered equally important in evidences combination, which gone against with the knowledge that different classifier has various performance due to diversity of classifiers. Therefore, how to determine the weights of individual classifier in order to get more accurate results becomes a question need.
The dempster shafer theory is a mathematical theory of evidence, which allows one to combine evidences from different sources and arrive at a belief function by taking into account all the available evidences. It is shown that application of the dempster shafer rule of combination in this case leads to an evaluation of masses of the combined bodies that is. The theory of belief functions, also referred to as evidence theory or dempster shafer theory. Data classification using the dempstershafer method arxiv.
Combining multiple query interface matchers using dempstershafer theory of evidence jun hong, zhongtian he and david a. Using the dempstershafer theory of evidence to resolve. Bell school of electronics, electrical engineering and computer science queens university belfast belfast bt7 1nn, uk fj. The dempster shafer theory of evidence is a powerful method for combining measures of evidence from different classifiers. Nguyen 1978 points out, soon after its introduction, t. Combining classifiers using the dempster shafer theory of. Nov 25, 2014 dempster s rule of combination can only be used when the bodies of evidence are assumed to be independent. Sentiment prediction based on dempstershafer theory of. On the independence requirement in dempstershafer theory. However, to use ds sensor fusion algorithm for robust application, we have to. Conflict management in dempstershafer theory ds theory is a hot topic in information fusion. Dempstershafer evidence combination rule generalization of equation eq. A new technique for combining multiple classifiers using the. Timedomain data fusion using weighted evidence and.
Modified dempstershafer ds evidence theory can handle. The goal of the combination of imperfect information is to nd an accurate information, easily interpretable, which can resume the information set to be combined. Dempster shafer theory does not require an assumption regarding the probability of the individual constituents of the set or interval. Alani a, deriche m 2002 a new technique for combining multiple classifiers using the dempstershafer theory of evidence. Conflict management is an open issue in dempster shafer evidence theory. Weighted evidence combination based on distance of. As a tool to manipulate an uncertain environment, dempstershafer ds evidence theory is an established system for uncertainty management 4,5. The dempstershafer theory of evidence accumulation is one of the main tools for combining data obtained from multiple sources. Since its introduction the very name causes confusion, a more general term often used is belief functions both used intermittently here. An important aspect of this theory is the combination of evidence obtained from multiple sources and the modeling of conflict between them.
Dempstershafer theory ds theory, also called belief function theory, as introduced and developed by dempster and shafer 1,2, has emerged from their works on statistical inference and uncertain reasoning. On the behavior of dempsters rule of combination and the. To each answer, the probability that it must be true is denoted as bel and the probability that it cannot be false is denoted as pl. In this section, we first present a brief description of dempster shafer ds theory and then describe the way in which we apply it to the score aggregation problem. Exploring the combination of dempstershafer theory and. A flexible rule for evidential combination in dempstershafer. Get addresses conflict management in an open world, where the frame of. First, the weight is determined based on the distance of evidence. This paper proposes a systematic approach to handle dependence in evidence theory. Theory of evidence and its implication for the rule of combination.
Dempster shafer is a theory of uncertainty that helps to quantify the degree to which some source of evidence supports a particular. Dempsters rule of combination can only be used when the bodies of evidence are assumed to be independent. A study of dempstershafers theory of evidence in comparison to classical probability combination scott j. 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. Section 4 provides a resume of some elementary facts of group and semigroup theory. The significant innovation of this framework is that it allows for the allocation of a probability mass to sets or intervals. Mass collaborationdriven method for recommending product. The dempstershafer theory is a mathematical theory of evidence, which allows one to combine evidences from different sources and arrive at a belief function by taking into account all the available evidences. The disjunctive rule of combination and the generalized bayesian theorem. In this paper, a new weighted evidence combination on the basis of the distance between evidence and entropy function is presented. Shafers book contains a wealth of significant results, and is a must for anyone who wants to do serious research on problems relating to the rules of combination of evidence in expert systems. This research focuses on dempstershafers evidence theory and the different similarity measures used to.
To develop an adequate understanding of this theory re quires considerable effort and a good background in proba bility theory. Simulation results showed that this method is successful in capturing the changes dynamic behavior in timedomain object classification. These indices are used to code the process of reasoning under uncertainty the combination of evidenceusing the dempstershafer theory. Oct 20, 2016 dempster shafer evidence theory is an efficient tool used in knowledge reasoning and decisionmaking under uncertain environments. In order to solve the above problems, this paper puts forward a mass collaborationdriven method for recommending product ideas based on dempstershafer theory of evidence dst, on the basis of taking full consideration of the characteristics of the large number of ideas and the large number of participants. A new conflict management method in dempstershafer theory. Classic works of the dempstershafer theory of belief functions. This report surveys a number of possible combination rules for dempstershafer structures and provides examples of the. A modified combination rule in generalized evidence theory. The application of the theory hinges on the use of a rule for combining evidence from different sources. Evidence combination dempstershafer theory provides a method to combine the previous measures of evidence of different sources. This theory offers an elegant theoretical framework for modeling uncertainty, and provides a method for combining distinct bodies of evidence collected.
Dempstershafer theory, which can be regarded as a generalisation of probability theory, is a widely used formalism for reasoning with uncertain information. The proposed approach is identified as two procedures. Weighted evidence combination based on distance of evidence. 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. Click download or read online button to get a mathematical theory of evidence book now. 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. Download a mathematical theory of evidence or read online books in pdf, epub, tuebl, and mobi format. All the bpas of a same question will constitute a ds evidence. Paradoxes source of conflicts in ds combination rule. Dempstershafer is a theory of uncertainty that helps to quantify the degree to which some source of evidence supports a particular. Bel is also marked as bpa basic probability assignment or mass function m. Expert systemsdempstershafer theory wikibooks, open books. Paradox elimination in dempstershafer combination rule with. Dempstershafer theory for combining in silico evidence and.
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. This paper presents a new classifier combination technique based on the dempstershafer theory of evidence. This report surveys a number of possible combination rules for dempster shafer structures and provides examples of the implementation of these rules for discrete and intervalvalued data. Dempstershafer evidence theory is an efficient tool used in knowledge reasoning and decisionmaking under uncertain environments. Application of dempstershafer theory to the in silico prediction of chemical toxicity based on qsar models and rulebased structural alerts demonstrates the advantages of this approach.
In this work we have proposed three novel algorithms to combine different biometric systems using the dst. It is shown that application of the dempstershafer rule of combination in this case leads to an evaluation of masses of the combined. Dempster shafer theory, which can be regarded as a generalisation of probability theory, is a widely used formalism for reasoning with uncertain information. If is the basic probability assignment provided by source, the combination. Dempstershafer theory for combining in silico evidence. This research focuses on dempstershafers evidence theory and the different similarity measures used to compare two separate bodies of evidence boes. The algorithm allows to carry out the reasoning with updating the evidence in much more efficient way than using the original. Section 5 investigates the semigroup structure of dempster shafer and states the main results relating it to bayes. Thus, many alternative combination rules have been proposed to address this issue. Dempsters rule of combination is sometimes interpreted as an approximate generalisation of bayes rule. By bridging fuzzy logic and probabilistic reasoning, the theory of belief functions.
On the independence requirement in dempstershafer theory for. A mathematical theory of evidence download ebook pdf, epub. Pdf implementing dempsters rule for hierarchical evidence. The dempster shafer theory of evidence accumulation is one of the main tools for combining data obtained from multiple sources. Conflict management in dempster shafer theory ds theory is a hot topic in information fusion. A simple view of the dempstershafer theory of evidence. Combination of evidence in dempstershafer theory unt.
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