Modified dempstershafer ds evidence theory can handle. 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 algorithm allows to carry out the reasoning with updating the evidence in much more efficient way than using the original. This paper presents a new classifier combination technique based on the dempstershafer theory of evidence. A new technique for combining multiple classifiers using the. It sho wn that application of the dempstershafer rule com bination. Sentiment prediction based on dempstershafer theory of evidence.
Bel is also marked as bpa basic probability assignment or mass function m. In this paper a special case of combination of two bodies of evidence with nonzero conflict coefficient is considered. Conflict management is an open issue in dempstershafer evidence theory. The dempster shafer theory of evidence accumulation is one of the main tools for combining data obtained from multiple sources. Weighted evidence combination based on distance of. However, when the evidence is severely conflicting, the result could be counterintuitive. Shafer combination creates a desired synergy between 2 bodies of knowledge, which improves the precision of the top. Dempster shafer is a theory of uncertainty that helps to quantify the degree to which some source of evidence supports a particular. Handling of dependence in dempstershafer theory su. A method of multiclassifier combination based on dempster. It is shown that application of the dempstershafer rule of combination in this case leads to an evaluation of masses of the combined.
Combining multiple query interface matchers using dempstershafer theory of evidence jun hong, zhongtian he and david a. Updating the evidence in the dempstershafer theory deepdyve. Nguyen 1978 points out, soon after its introduction, t. The proposed approach is identified as two procedures. 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. 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.
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. 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. Dempstershafer evidence combination rule generalization of equation eq. Dempstershafer theory for combining in silico evidence and. Bell school of electronics, electrical engineering and computer science queens university belfast belfast bt7 1nn, uk fj. On the dempstershafer framework and new combination rules.
Evidence combination dempstershafer theory provides a method to combine the previous measures of evidence of different sources. Gordon and others published the dempstershafer theory of evidence find, read and cite all the research you. However, to use ds sensor fusion algorithm for robust application, we have to. Section 5 investigates the semigroup structure of dempster shafer and states the main results relating it to bayes. The paper presents an algorithm for translating the subontologies containing con. The dempstershafer theory is a natural, important and useful extension of classical probability theory. 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 research focuses on dempstershafers evidence theory and the different similarity measures used to compare two separate bodies of evidence boes. The application of the theory hinges on the use of a rule for combining evidence from different sources. Jeffreylike rules of conditioning for the dempstershafer theory of. Simulation results showed that this method is successful in capturing the changes dynamic behavior in timedomain object classification. The application of dempstershafer theory demonstrated. Weighted evidence combination based on distance of evidence. Shafer theory to combine the results obtained from individual sources to find a final unified ranking. 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. Dempstershafer evidence theory is an efficient tool used in knowledge reasoning and decisionmaking under uncertain environments. 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. On the independence requirement in dempstershafer theory for.
Combining multiple query interface matchers using dempster. First, the weight is determined based on the distance of evidence. 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. Dempsters rule of combination can only be used when the bodies of evidence are assumed to be independent. 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. A new conflict management method in dempstershafer theory.
This paper presents a new classifier combination technique based on the dempster shafer theory of evidence. This theory offers an elegant theoretical framework for modeling uncertainty, and provides a method for combining distinct bodies of evidence collected. Pdf implementing dempsters rule for hierarchical evidence. Dempstershafer theory for combining in silico evidence. Pdf combination of evidence in dempstershafer theory. Dempstershafer theory, introduced and developed by dempster and shafer 6,7,8, has many merits by contrast to bayesian probability theory. Download a mathematical theory of evidence or read online books in pdf, epub, tuebl, and mobi format. As a tool to manipulate an uncertain environment, dempstershafer ds evidence theory is an established system for uncertainty management 4,5. 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. Combination of evidence in dempstershafer theory core. Dempsters rule of combination is sometimes interpreted as an approximate generalisation of bayes rule. 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.
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. Section 4 provides a resume of some elementary facts of group and semigroup theory. 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. On the behavior of dempsters rule of combination and the. A simple view of the dempstershafer theory of evidence and its. The significant innovation of this framework is that it allows for the allocation of a probability mass to sets or intervals. 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. Nov 25, 2014 dempster s rule of combination can only be used when the bodies of evidence are assumed to be independent. Oct 20, 2016 dempster shafer evidence theory is an efficient tool used in knowledge reasoning and decisionmaking under uncertain environments. Dempstershafer theory does not require an assumption regarding the probability of the individual constituents of the set or interval. Shortliffe the drawbacks of pure probabilistic methods and of the certainty factor model have led us in recent years to consider alternate approaches. Alani a, deriche m 2002 a new technique for combining multiple classifiers using the dempstershafer theory of evidence. Theory of evidence and its implication for the rule of combination.
As a more flexible mathematical tool, dempstershafer theory not only combines with other mathematical frameworks 1518, but also combines with. Timedomain data fusion using weighted evidence and. A mathematical theory of evidence download ebook pdf. 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. Dempstershafer theory, data classification, dempsters rule of combination. These indices are used to code the process of reasoning under uncertainty the combination of evidenceusing the dempstershafer theory. The limitations of the original ds combination rule and works to eliminate them are discussed in section 4. The application of dempstershafer theory demonstrated with justi. If is the basic probability assignment provided by source, the combination. Dempster shafer theory does not require an assumption regarding the probability of the individual constituents of the set or interval. 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.
Paradox elimination in dempstershafer combination rule with. The dempstershafer theory of evidence accumulation is one of the main tools for combining data obtained from multiple sources. In this work we have proposed three novel algorithms to combine different biometric systems using the dst. A mathematical theory of evidence download ebook pdf, epub. The initial work introducing dempstershafer ds theory is found in dempster 1967 and shafer 1976. A flexible rule for evidential combination in dempstershafer. Pdf the dempstershafer theory of evidence researchgate. Dempsters combination rule in dempstershafer theory of evidence is widely used to combine multiple pieces of evidence. 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 5 investigates the semigroup structure of dempstershafer and states the main results relating it to bayes. All the bpas of a same question will constitute a ds evidence.
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. This research focuses on dempstershafers evidence theory and the different similarity measures used to. A simple view of the dempstershafer theory of evidence and. Using the dempstershafer theory of evidence to resolve. On the independence requirement in dempstershafer theory. A study of dempstershafers theory of evidence in comparison to classical probability combination scott j. Dempstershafer theory, which can be regarded as a generalisation of probability theory, is a widely used formalism for reasoning with uncertain information. This site is like a library, use search box in the widget to get ebook that you want. 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. An important aspect of this theory is the combination of evidence obtained from. 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. To apply data fusion in timedomain based on dempstershafer ds combination rule, an 8step algorithm with novel entropy function is proposed. Conflict management is an open issue in dempster shafer evidence theory. The 8step algorithm is applied to timedomain to achieve the sequential combination of timedomain data.
Get addresses conflict management in an open world, where the. Sentiment prediction based on dempstershafer theory of. An algorithm for updating the evidence in the dempstershafer theory is presented. Expert systemsdempstershafer theory wikibooks, open books. The dempstershafer theory of evidence is a powerful method for combining measures of evidence from different classifiers. Combining classifiers using the dempster shafer theory of. Ds is a mathematical theory of evidence based on belief functions and plausible. Dempstershafer theory offers an alternative to traditional probabilistic theory for the mathematical representation of uncertainty. The dempster shafer theory of evidence is a powerful method for combining measures of evidence from different classifiers. 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.
Conflict management in dempstershafer theory ds theory is a hot topic in information fusion. In this paper, a new weighted evidence combination on the basis of the distance between evidence and entropy function is presented. A study of dempstershafers theory of evidence in comparison. The application of dempstershafer theory demonstrated with. Zadeh compufer science division, universify of california, berkeley, california 94720 the emergence of expert systems as one of the major ar. The theory of belief functions, also referred to as evidence theory or dempster shafer theory. Special issue on the dempstershafer theory of evidence. Dempster shafer theory, which can be regarded as a generalisation of probability theory, is a widely used formalism for reasoning with uncertain information. A simple view of the dempstershafer theory of evidence.
Therefore, how to determine the weights of individual classifier in order to get more accurate results becomes a question need. The dempstershafer theory of evidence is a formalism, which allows appropriate interpretation of extractors con. Paradox elimination in dempstershafer combination rule. Click download or read online button to get a mathematical theory of evidence book now. This paper proposes a systematic approach to handle dependence in evidence theory. In this context the dempster shafer theory of evidence dst has shown some promising results. Classic works of the dempstershafer theory of belief functions. The dempstershafer theory of evidence jean gordon and edward h. Data classification using the dempstershafer method arxiv. The disjunctive rule of combination and the generalized bayesian theorem. Conflict management in dempster shafer theory ds theory is a hot topic in information fusion. Paradoxes source of conflicts in ds combination rule. A simple view of the dempstershafer theory of evidence and its implication for the rule of combination lotfi a.
There is, however, a simple way of approach ing the dempster shafer theory that only requires a min. Combination of evidence in dempstershafer theory unt. By bridging fuzzy logic and probabilistic reasoning, the theory of belief functions. 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. Since its introduction the very name causes confusion, a more general term often used is belief functions both used intermittently here. Particularly appealing is the mathematical theory of evidence developed by arthur dempster. Thus, many alternative combination rules have been proposed to address this issue. 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. 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. Handling of dependence in dempstershafer theory su 2015.
Get addresses conflict management in an open world, where the frame of. Timedomain data fusion using weighted evidence and dempster. It sho wn that application of the dempstershafer rule com bination in this case leads to ev aluation of masses. This report surveys a number of possible combination rules for dempstershafer structures and provides examples of the. A case of combination of evidence in the dempstershafer theory. A new technique for combining multiple classifiers using. To develop an adequate understanding of this theory re quires considerable effort and a good background in proba bility theory. Dempstershafer ds theory 1 is an effective tool to make a decision from several answers with ambiguity. A flexible rule for evidential combination in dempster. A modified combination rule in generalized evidence theory. Dempstershafer is a theory of uncertainty that helps to quantify the degree to which some source of evidence supports a particular. A case of combination of evidence in the dempstershafer.
1427 1358 1549 694 1272 1037 1149 161 153 428 954 663 379 1189 1559 969 57 149 38 293 817 253 974 313 713 1120 583 717 378 19 478 1484 967 954 1142 999 1483 1042 793 456