This paper presents a new classifier combination technique based on the dempstershafer theory of evidence. The algorithm allows to carry out the reasoning with updating the evidence in much more efficient way than using the original. Dempsters rule of combination is sometimes interpreted as an approximate generalisation of bayes rule. Expert systemsdempstershafer theory wikibooks, open books. Pdf combination of evidence in dempstershafer theory. 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. Get addresses conflict management in an open world, where the. Theory of evidence and its implication for the rule of combination.
It is shown that application of the dempstershafer rule of combination in this case leads to an evaluation of masses of the combined. Dempstershafer evidence combination rule generalization of equation eq. Dempsters rule of combination can only be used when the bodies of evidence are assumed to be independent. Click download or read online button to get a mathematical theory of evidence book now. 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. Modified dempstershafer ds evidence theory can handle. Conflict management in dempstershafer theory ds theory is a hot topic in information fusion. Conflict management is an open issue in dempster shafer evidence theory. Shortliffe the drawbacks of pure probabilistic methods and of the certainty factor model have led us in recent years to consider alternate approaches.
Section 5 investigates the semigroup structure of dempster shafer and states the main results relating it to bayes. 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. 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. The 8step algorithm is applied to timedomain to achieve the sequential combination of timedomain data. Dempstershafer theory, data classification, dempsters rule of combination. Combining multiple query interface matchers using dempstershafer theory of evidence jun hong, zhongtian he and david a.
The proposed approach is identified as two procedures. 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. 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. Combination of evidence in dempstershafer theory unt. Combination of evidence in dempstershafer theory core. Evidence combination dempstershafer theory provides a method to combine the previous measures of evidence of different sources. Dempstershafer theory, introduced and developed by dempster and shafer 6,7,8, has many merits by contrast to bayesian probability theory. A simple view of the dempstershafer theory of evidence and its implication for the rule of combination lotfi a. On the independence requirement in dempstershafer theory for. A flexible rule for evidential combination in dempstershafer. 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. The disjunctive rule of combination and the generalized bayesian theorem. The dempstershafer theory of evidence is a formalism, which allows appropriate interpretation of extractors con. Pdf implementing dempsters rule for hierarchical evidence.
Simulation results showed that this method is successful in capturing the changes dynamic behavior in timedomain object classification. Sentiment prediction based on dempstershafer theory of evidence. Using the dempstershafer theory of evidence to resolve. Gordon and others published the dempstershafer theory of evidence find, read and cite all the research you. A new technique for combining multiple classifiers using. This paper presents a new classifier combination technique based on the dempster shafer theory of evidence. These indices are used to code the process of reasoning under uncertainty the combination of evidenceusing the dempstershafer theory. This theory may be considered as a generalization of the probability theory. Conflict management in dempster shafer theory ds theory is a hot topic in information fusion. A study of dempstershafers theory of evidence in comparison to classical probability combination scott j. Combining multiple query interface matchers using dempster.
Dempstershafer theory for combining in silico evidence. A study of dempstershafers theory of evidence in comparison. The application of dempstershafer theory demonstrated with. Updating the evidence in the dempstershafer theory deepdyve. On the independence requirement in dempstershafer theory. 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. A new technique for combining multiple classifiers using the. In this work we have proposed three novel algorithms to combine different biometric systems using the dst. The dempster shafer theory of evidence accumulation is one of the main tools for combining data obtained from multiple sources. Combining classifiers using the dempster shafer theory of. As a more flexible mathematical tool, dempstershafer theory not only combines with other mathematical frameworks 1518, but also combines with. There is no good practice that can be generally accepted until the presence of generalized evidence theory get.
All the bpas of a same question will constitute a ds evidence. Shafer theory to combine the results obtained from individual sources to find a final unified ranking. This report surveys a number of possible combination rules for dempstershafer structures and provides examples of the. First, the weight is determined based on the distance of evidence. The dempstershafer theory of evidence is a powerful method for combining measures of evidence from different classifiers. Paradox elimination in dempstershafer combination rule with. 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. Shafer combination creates a desired synergy between 2 bodies of knowledge, which improves the precision of the top.
A mathematical theory of evidence download ebook pdf, epub. By bridging fuzzy logic and probabilistic reasoning, the theory of belief functions. Timedomain data fusion using weighted evidence and dempster. Section 4 provides a resume of some elementary facts of group and semigroup theory. Zadeh compufer science division, universify of california, berkeley, california 94720 the emergence of expert systems as one of the major ar. It sho wn that application of the dempstershafer rule com bination. Bell school of electronics, electrical engineering and computer science queens university belfast belfast bt7 1nn, uk fj. 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. Dempstershafer theory for combining in silico evidence and. The paper presents an algorithm for translating the subontologies containing con. 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. Paradoxes source of conflicts in ds combination rule. A simple view of the dempstershafer theory of evidence and.
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. It sho wn that application of the dempstershafer rule com bination in this case leads to ev aluation of masses. 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. This research focuses on dempstershafers evidence theory and the different similarity measures used to. The dempster shafer theory of evidence jean gordon and edward h. Dempstershafer theory offers an alternative to traditional probabilistic theory for the mathematical representation of uncertainty. A method of multiclassifier combination based on dempster. 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.
Dempstershafer is a theory of uncertainty that helps to quantify the degree to which some source of evidence supports a particular. Special issue on the dempstershafer theory of evidence. 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. Exploring the combination of dempstershafer theory and. Since its introduction the very name causes confusion, a more general term often used is belief functions both used intermittently here. On the dempstershafer framework and new combination rules. The application of dempstershafer theory demonstrated. Pdf the dempstershafer theory of evidence researchgate. On the behavior of dempsters rule of combination and the. 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. 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. Data classification using the dempstershafer method arxiv. Download a mathematical theory of evidence or read online books in pdf, epub, tuebl, and mobi format. Ds is a mathematical theory of evidence based on belief functions and plausible.
Timedomain data fusion using weighted evidence and. Thus, many alternative combination rules have been proposed to address this issue. This theory offers an elegant theoretical framework for modeling uncertainty, and provides a method for combining distinct bodies of evidence collected. Conflict management is an open issue in dempstershafer evidence theory. A case of combination of evidence in the dempstershafer theory.
In this paper a special case of combination of two bodies of evidence with nonzero conflict coefficient is considered. Get addresses conflict management in an open world, where the frame of. In this paper, a new weighted evidence combination on the basis of the distance between evidence and entropy function is presented. However, to use ds sensor fusion algorithm for robust application, we have to. A case of combination of evidence in the dempstershafer. The significant innovation of this framework is that it allows for the allocation of a probability mass to sets or intervals.
Dempster shafer theory does not require an assumption regarding the probability of the individual constituents of the set or interval. Dempstershafer ds theory 1 is an effective tool to make a decision from several answers with ambiguity. 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. There is, however, a simple way of approach ing the dempster shafer theory that only requires a min. A new conflict management method in dempstershafer theory. However, when the evidence is severely conflicting, the result could be counterintuitive.
Dempstershafer theory does not require an assumption regarding the probability of the individual constituents of the set or interval. The theory of belief functions, also referred to as evidence theory or dempster shafer theory. 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. 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. Therefore, how to determine the weights of individual classifier in order to get more accurate results becomes a question need. The dempstershafer theory is a natural, important and useful extension of classical probability theory. Dempsters combination rule in dempstershafer theory of evidence is widely used to combine multiple pieces of evidence. The limitations of the original ds combination rule and works to eliminate them are discussed in section 4. The application of the theory hinges on the use of a rule for combining evidence from different sources.
Section 5 investigates the semigroup structure of dempstershafer and states the main results relating it to bayes. In this context the dempster shafer theory of evidence dst has shown some promising results. Oct 20, 2016 dempster shafer evidence theory is an efficient tool used in knowledge reasoning and decisionmaking under uncertain environments. Handling of dependence in dempstershafer theory su 2015. The dempstershafer theory of evidence jean gordon and edward h. This site is like a library, use search box in the widget to get ebook that you want. This research focuses on dempstershafers evidence theory and the different similarity measures used to compare two separate bodies of evidence boes. Paradox elimination in dempstershafer combination rule. The dempstershafer theory of evidence accumulation is one of the main tools for combining data obtained from multiple sources. 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. This paper proposes a systematic approach to handle dependence in evidence theory.
The initial work introducing dempstershafer ds theory is found in dempster 1967 and shafer 1976. 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. 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. A modified combination rule in generalized evidence theory. Dempster shafer theory, which can be regarded as a generalisation of probability theory, is a widely used formalism for reasoning with uncertain information. Nov 25, 2014 dempster s rule of combination can only be used when the bodies of evidence are assumed to be independent. As a tool to manipulate an uncertain environment, dempstershafer ds evidence theory is an established system for uncertainty management 4,5. A mathematical theory of evidence download ebook pdf. 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 dempster shafer theory of evidence is a powerful method for combining measures of evidence from different classifiers.
A simple view of the dempstershafer theory of evidence and its. Weighted evidence combination based on distance of. Dempstershafer evidence theory is an efficient tool used in knowledge reasoning and decisionmaking under uncertain environments. A flexible rule for evidential combination in dempster. Bel is also marked as bpa basic probability assignment or mass function m. Dempster shafer is a theory of uncertainty that helps to quantify the degree to which some source of evidence supports a particular. Mass collaborationdriven method for recommending product. Jeffreylike rules of conditioning for the dempstershafer theory of. Sentiment prediction based on dempstershafer theory of. 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.
To apply data fusion in timedomain based on dempstershafer ds combination rule, an 8step algorithm with novel entropy function is proposed. Weighted evidence combination based on distance of evidence. If is the basic probability assignment provided by source, the combination. Particularly appealing is the mathematical theory of evidence developed by arthur dempster. To develop an adequate understanding of this theory re quires considerable effort and a good background in proba bility theory. Classic works of the dempstershafer theory of belief functions. Dempstershafer theory, which can be regarded as a generalisation of probability theory, is a widely used formalism for reasoning with uncertain information.
1166 1470 435 269 86 99 1349 1363 741 314 93 694 1247 966 934 1588 438 1049 1319 793 1108 1110 1065 1188 622 747 803 1386