This research focuses on dempstershafers evidence theory and the different similarity measures used to compare two separate bodies of evidence boes. The dempstershafer theory dst of evidence originated in the work of dempster on the theory of probabilities with upper and lower bounds. Combination of acoustic classifiers based on dempstershafer. A simple view of the dempstershafer theory of evidence. Clearly, this result is counterintuitive since it implies complete support for a diagnosis that both a and b considered highly improbable. They are deployed to trap hackers in order to help security professionals capture, control, and analyze malicious internet attacks and other activities of hackers. Pdf dempstershafer theory offers an alternative to traditional probabilistic.
The dempstershafer theory of evidence accumulation is one of the main tools for combining data obtained from multiple sources. A mathematical theory of evidence turns 40 glenn shafer. Dempstershafer theory for combining in silico evidence. A case of combination of evidence in the dempstershafer theory. 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.
Situations and evidence for identity using dempstershafer. Combination of evidence in dempster shafer theory sandia. It sho wn that application of the dempstershafer rule com bination in this case leads to ev. The dempstershafer theory of evidence jean gordon and edward h. The application of the theory hinges on the use of a rule for combining evidence from different sources.
A simple view of the dempstershafer theory of evidence and. Contextdependent combination of sensor information in probability0. Combination of acoustic classifiers based on dempster. Using max and min combination operators hard to represent cumulative evidence bayesian probability appropriate but has disadvantages dempstershafer theory of evidence bayesian probability theory.
Abstract evidence theory is best suitable for the problem of red system operation is a real time, dynamic decision process, a. Compared with bayesian theory, the dempster shafer theory of evidence feels closer to our human perception and reasoning processes. It has since been extended by numerous authors and popularised, but only to a degree, in the literature on artificial intelligence ai and expert systems, as a technique for modelling reasoning under. Fundamental difference between ds theory and probability theory is the treatment of ignorance. The dempstershafer theory dst is a mathematical theory of evidence. This theory offers an elegant theoretical framework for modeling uncertainty, and provides a method for combining distinct bodies of evidence collected.
June 7, 2016 abstract the book that launched the dempstershafer theory of belief functions appeared 40 years ago. When a new event occurs, the nodes in the neighborhood of the event sense and then send many packets to the sink node. The theory allows one to combine evidence from different sources and arrive at a degree of belief represented by a mathematical object called belief function that takes into account all the available evidence. Contextdependent combination of sensor information in. Approximations for decision making in the dempstershafer. To develop an adequate understanding of this theory re quires considerable effort and a good background in proba bility theory. Dempster shafer theory, which can be regarded as a generalisation of probability theory, is a widely used formalism for reasoning with uncertain information. A study of dempstershafers theory of evidence in comparison to classical probability combination scott j. Mel siegel, chair daniel siewiorek jie yang wolfgang grimm, robert bosch corporation the robotics institute. Different combination are tested in asr experiments both in matched and mismatched conditions and compared with more conventional probability combination rules. An important aspect of this theory is the combination of evidence. Pdf the dempstershafer theory of evidence researchgate. Shortliffe the drawbacks of pure probabilistic methods and of the certainty factor model have led us in recent years to consider alternate approaches. A novel information fusion method based on dempstershafer.
In this paper we investigate combination of neural net based classifiers using dempstershafer theory of evidence. Exploring the combination of dempstershafer theory and. 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. Section 5 investigates the semigroup structure of dempstershafer and states the main results relating it to bayes. As a result, dsbased information fusion is very popular in decisionmaking applications, but original ds theory produces counterintuitive results when combining highly conflicting evidences from multiple sensors. A case of combination of evidence in the dempstershafer. Its capability to assign uncertainty or ignorance to propositions is a powerful tool for dealing with. 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. Dempstershafer evidence combining for antihoneypot. The key contribution of this thesis is introducing the dempster shafer theory of evidence as a generalizable sensor fusion solution to overcome the typical context sensing difficulties, wherein some of the available information items are subjective.
Moreover, it should be remove redundant packets in order to lower the average energy consumption. Under some assumptions, combination rule resembles a product of errors rule observed in human speech perception. Dempstershafer theory offers an alternative to traditional probabilistic theory for the. A novel information fusion method based on dempstershafer evidence theory for con. It deals with the representation of nonprobabilistic uncertainty about sets of facts belief function and the accumulation of evidence stemming from independent sources dempsters rule of evidence combination and with reasoning under. As a more flexible mathematical tool, dempstershafer theory not only combines with other mathematical frameworks 1518, but also. In this work we have proposed three novel algorithms to combine different biometric systems using the dst. Different combination are tested in asr experiments both in matched and mismatched conditions and compared with more conventional probability. In ds theory, the degree of belief in evidence is analogous to the mass of a physical object mass of evidence supports a belief. An important aspect of this theory is the combination of evidence obtained from multiple sources and the modeling of conflict between them.
This paper presents an event recognition framework, based on dempstershafer theory, that combines evidence of events from lowlevel computer vision analytics. This theory may be considered as a generalization of the probability theory. Here we purposed a temporal weighted evidence combination method together with the procedure of application. A new technique for combining multiple classifiers using. This intellectual autobiography looks back on how i came to write the book and how its ideas played out in my later work. 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. Section 4 provides a resume of some elementary facts of group and semigroup theory. The application of dempstershafer theory demonstrated with. Sensor data fusion for contextaware computing using. A new technique for combining multiple classifiers using the.
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. A simple view of the dempstershafer theory of evidence and its implication for the rule of combination lotfi a. The dempster shafer theory of evidence accumulation is one of the main tools for combining data obtained from multiple sources. This theory offers an elegant theoretical framework for modeling uncertainty, and provides a method for combining distinct bodies of evidence collected from. Eeg data in particular, we show what types of evidence can a ect discrete variables such as a medical diagnosis and build a simple and computationally e cient evidence combination model based on the dempster shafer theory. 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 24. A study of dempstershafers theory of evidence in comparison. This paper presents a new classifier combination technique based on the dempstershafer theory of evidence. An important aspect of this theory is the combination of evidence obtained from. Novel data fusion algorithm based on eventdriven and. Sensor data fusion for contextaware computing using dempstershafer theory huadong wu cmuritr0352 submitted in partial fulfillment of the requirements for the degree of doctor of philosophy in robotics thesis committee.
In this paper a special case of combination of two bodies of evidence with nonzero conflict coefficient is considered. Paradox elimination in dempstershafer combination rule. There is, however, a simple way of approach ing the dempster shafer theory that only requires a min. Any suspects exceeding the threshold value would be suspected of being guilty. For example, in the search for the identity of an infecting organism. Section 5 investigates the semigroup structure of dempster shafer and states the main results relating it to bayes. 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. Central to dempstershafer theory is the rule of combination of beliefs. Operation condition monitoring using temporal weighted. Situations and evidence for identity using dempstershafer theory. The burden of our theory is that this rule dempsters rule of combination corresponds to the pooling of evidence. Different than the bayes rule, dempstershafer theory represents evidence as possibilistic belief. It has since been extended by numerous authors and popularised, but only to a degree, in the literature on artificial intelligence ai and expert systems, as a technique for modelling.
Gordon and others published the dempstershafer theory of. 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. Expert systemsdempstershafer theory wikibooks, open books. Evidence combination for incremental decisionmaking.
It is shown that application of the dempstershafer rule of combination in this case leads to an evaluation of. This paper reports on a largescale experiment for the evaluation of a formal querybiased combination of evidence mechanism. Video event recognition by dempstershafer theory xin hong, yan huang, wenjun ma, paul miller, weiru liu and huiyu zhou 1 abstract. The dempster shafer theory of evidence jean gordon and edward h.
The dempstershafer theory of evidence is a formalism, which allows appropriate interpretation of extractors con. Particularly appealing is the mathematical theory of evidence developed by arthur dempster. Dempster in the context of statistical inference, the theory was later. The paper presents an algorithm for translating the subontologies containing con. 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 section 4, we illustrate how dempstershafer combination of evidence can be used to. In this work we have proposed three novel algorithms. Modified dempstershafer ds evidence theory can handle conflicting sensor inputs and can be applied without any prior information. Zadeh compufer science division, universify of california, berkeley, california 94720 the emergence of expert systems as one of the major ar. Evidence combination dempstershafer theory provides a method to combine the previous measures of evidence of different sources.
Future work we will consider different combination rules and applications of dempster shafer theory, look at weighting evidence, increase our set of scenarios. The application of dempstershafer theory demonstrated with justi. Dempster shafer theory of evidence is introduced, and the problem of application dst to online diagnosis for operation condition monitoring and failure detection and recognition is analyzed. On the behavior of dempsters rule of combination and the. Byapplyingdempstersrule,wewouldconcludethat p1 hasabraintumour with probability 1.
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 paper presents a new classifier combination technique based on the dempster shafer theory of evidence. Dempstershafer theory for a querybiased combination of evidence. Such circumstances need their networks to be simultaneously reliable and eventdriven. Honeypots are network surveillance architectures designed to resemble easytocompromise computer systems. Dempstershafer theory for combining in silico evidence and. The application of dempstershafer theory demonstrated. If is the basic probability assignment provided by source, the combination. It sho wn that application of the dempstershafer rule com bination in this case leads to ev aluation of masses. A botnet is an army of compromised computers controlled by a bot herder and used for illicit financial gain. Dempstershafer evidence combination rule generalization of equation eq. An important aspect of this theory is the combination of evidence obtained from multiple sources and the modeling of conflict. It is much newer theory, originating in the work of a. The dempstershafer theory of evidence is a powerful method for combining measures of evidence from different classifiers.
Combination of evidence in dempstershafer theory unt. We use the dempstershafer theory of evidence to combine optimally results obtained by content and link analyses on the web. In this paper we investigate combination of neural net based classifiers using dempster shafer theory of evidence. Shafer, 1976 is one of the main points of criticism this formalism has to face. The dempster shafer theory of evidence is a powerful method for combining measures of evidence from different classifiers. 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. Pdf combination of evidence in dempstershafer theory. Evidence combination for incremental decisionmaking processes. Dempster with extensions by glen shafer in 1987 shafer, 1976. Sensor data fusion for contextaware computing using dempster. Combining classifiers using the dempster shafer theory of. In this context the dempster shafer theory of evidence dst has shown some promising results. Eeg data in particular, we show what types of evidence can a ect discrete variables such as a medical diagnosis and build a simple and computationally e cient evidence combination model based on the dempstershafer theory.
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