Combination of evidence in dempster-shafer theory pdf files

In this work we have proposed three novel algorithms. A case of combination of evidence in the dempstershafer. The dempstershafer theory dst of evidence originated in the work of dempster on the theory of probabilities with upper and lower bounds. This theory may be considered as a generalization of the probability theory. In section 4, we illustrate how dempstershafer combination of evidence can be used to. Combining classifiers using the dempster shafer theory of. A new technique for combining multiple classifiers using the. 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. The application of dempstershafer theory demonstrated with justi. An important aspect of this theory is the combination of evidence.

A novel information fusion method based on dempstershafer. They are deployed to trap hackers in order to help security professionals capture, control, and analyze malicious internet attacks and other activities of hackers. Combination of evidence in dempstershafer theory unt. Under some assumptions, combination rule resembles a product of errors rule observed in human speech perception. Dempstershafer theory for combining in silico evidence and. Sensor data fusion for contextaware computing using dempster. An important aspect of this theory is the combination of evidence obtained from multiple sources and the modeling of conflict between them. Situations and evidence for identity using dempstershafer theory. Particularly appealing is the mathematical theory of evidence developed by arthur dempster. 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. 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. When a new event occurs, the nodes in the neighborhood of the event sense and then send many packets to the sink node.

Pdf combination of evidence in dempstershafer theory. To develop an adequate understanding of this theory re quires considerable effort and a good background in proba bility theory. Future work we will consider different combination rules and applications of dempster shafer theory, look at weighting evidence, increase our set of scenarios. A simple view of the dempstershafer theory of evidence and its implication for the rule of combination lotfi a. 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. It is much newer theory, originating in the work of a. 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. Novel data fusion algorithm based on eventdriven and. We use the dempstershafer theory of evidence to combine optimally results obtained by content and link analyses on the web. The dempster shafer theory of evidence jean gordon and edward h. It is shown that application of the dempstershafer rule of combination in this case leads to an evaluation of. Compared with bayesian theory, the dempster shafer theory of evidence feels closer to our human perception and reasoning processes.

A study of dempstershafers theory of evidence in comparison. As a more flexible mathematical tool, dempstershafer theory not only combines with other mathematical frameworks 1518, but also. 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. In this context the dempster shafer theory of evidence dst has shown some promising results. A botnet is an army of compromised computers controlled by a bot herder and used for illicit financial gain. The burden of our theory is that this rule dempsters rule of combination corresponds to the pooling of evidence. The paper presents an algorithm for translating the subontologies containing con. Using the dempstershafer theory of evidence to resolve abox. Combination of acoustic classifiers 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.

The application of dempstershafer theory demonstrated. 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. Moreover, it should be remove redundant packets in order to lower the average energy consumption. Dempstershafer theory, which can be regarded as a generalisation of probability theory, is a widely used formalism for reasoning with uncertain information. An important aspect of this theory is the combination of evidence obtained from multiple sources and the modeling of conflict.

Section 4 provides a resume of some elementary facts of group and semigroup theory. Video event recognition by dempstershafer theory xin hong, yan huang, wenjun ma, paul miller, weiru liu and huiyu zhou 1 abstract. In this paper we investigate combination of neural net based classifiers using dempstershafer theory of evidence. 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 dempster shafer theory of evidence is a powerful method for combining measures of evidence from different classifiers. Shafer, 1976 is one of the main points of criticism this formalism has to face. Any suspects exceeding the threshold value would be suspected of being guilty.

Dempster shafer theory, which can be regarded as a generalisation of probability theory, is a widely used formalism for reasoning with uncertain information. Combination of acoustic classifiers based on dempstershafer. Abstract evidence theory is best suitable for the problem of red system operation is a real time, dynamic decision process, a. Contextdependent combination of sensor information in probability0. 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. There is, however, a simple way of approach ing the dempster shafer theory that only requires a min. 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. Dempster with extensions by glen shafer in 1987 shafer, 1976. A simple view of the dempstershafer theory of evidence.

Its capability to assign uncertainty or ignorance to propositions is a powerful tool for dealing with. This theory offers an elegant theoretical framework for modeling uncertainty, and provides a method for combining distinct bodies of evidence collected. This research focuses on dempstershafers evidence theory and the different similarity measures used to compare two separate bodies of evidence boes. Combination of evidence in dempster shafer theory sandia. Different than the bayes rule, dempstershafer theory represents evidence as possibilistic belief. Using the dempstershafer theory of evidence to resolve.

Contextdependent combination of sensor information in. A study of dempstershafers theory of evidence in comparison to classical probability combination scott j. Modified dempstershafer ds evidence theory can handle conflicting sensor inputs and can be applied without any prior information. 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. Here we purposed a temporal weighted evidence combination method together with the procedure of application. Evidence combination dempstershafer theory provides a method to combine the previous measures of evidence of different sources. Operation condition monitoring using temporal weighted. On the behavior of dempsters rule of combination and the. It sho wn that application of the dempstershafer rule com bination in this case leads to ev aluation of masses. 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 evidence combination rule generalization of equation eq.

Using max and min combination operators hard to represent cumulative evidence bayesian probability appropriate but has disadvantages dempstershafer theory of evidence bayesian probability 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 2, 3, 4. The application of dempstershafer theory demonstrated with. Pdf the dempstershafer theory of evidence researchgate. Section 5 investigates the semigroup structure of dempstershafer and states the main results relating it to bayes. In this paper we investigate combination of neural net based classifiers using dempster shafer theory of evidence. Zadeh compufer science division, universify of california, berkeley, california 94720 the emergence of expert systems as one of the major ar. Expert systemsdempstershafer theory wikibooks, open books. 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. 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. Such circumstances need their networks to be simultaneously reliable and eventdriven.

Fundamental difference between ds theory and probability theory is the treatment of ignorance. This paper presents a new classifier combination technique based on the dempstershafer theory of evidence. Different combination are tested in asr experiments both in matched and mismatched conditions and compared with more conventional probability. The application of the theory hinges on the use of a rule for combining evidence from different sources. 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. Evidence combination for incremental decisionmaking. An important aspect of this theory is the combination of evidence obtained from. This theory offers an elegant theoretical framework for modeling uncertainty, and provides a method for combining distinct bodies of evidence collected from.

It sho wn that application of the dempstershafer rule com bination in this case leads to ev. If is the basic probability assignment provided by source, the combination. Central to dempstershafer theory is the rule of combination of beliefs. 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 dempster shafer theory of evidence accumulation is one of the main tools for combining data obtained from multiple sources. Different combination are tested in asr experiments both in matched and mismatched conditions and compared with more conventional probability combination rules. In a narrow sense, the term dempstershafer theory refers to the original conception of the theory by dempster and shafer. Shortliffe the drawbacks of pure probabilistic methods and of the certainty factor model have led us in recent years to consider alternate approaches. This paper presents a new classifier combination technique based on the dempster shafer theory of evidence. Byapplyingdempstersrule,wewouldconcludethat p1 hasabraintumour with probability 1. Mel siegel, chair daniel siewiorek jie yang wolfgang grimm, robert bosch corporation the robotics institute. For example, in the search for the identity of an infecting organism.

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. Evidence combination for incremental decisionmaking processes. An alternative to the bayesian theory of evidence is dempstershafer theory which produces results similar to bayesian theory. The dempstershafer theory dst is a mathematical theory of evidence. The dempstershafer theory of evidence jean gordon and edward h. 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. This paper reports on a largescale experiment for the evaluation of a formal querybiased combination of evidence mechanism. A novel information fusion method based on dempstershafer evidence theory for con. A mathematical theory of evidence turns 40 glenn shafer. Approximations for decision making in the dempstershafer. 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. In ds theory, the degree of belief in evidence is analogous to the mass of a physical object mass of evidence supports a belief.

Section 5 investigates the semigroup structure of dempster shafer and states the main results relating it to bayes. The dempstershafer theory of evidence is a formalism, which allows appropriate interpretation of extractors con. Gordon and others published the dempstershafer theory of. 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 theory for combining in silico evidence. Sensor data fusion for contextaware computing using.

Dempstershafer theory for a querybiased combination of evidence. June 7, 2016 abstract the book that launched the dempstershafer theory of belief functions appeared 40 years ago. A simple view of the dempstershafer theory of evidence and. In this work we have proposed three novel algorithms to combine different biometric systems using the dst. A case of combination of evidence in the dempstershafer theory. Dempstershafer theory offers an alternative to traditional probabilistic theory for the.

Honeypots are network surveillance architectures designed to resemble easytocompromise computer systems. The dempstershafer theory of evidence accumulation is one of the main tools for combining data obtained from multiple sources. Situations and evidence for identity using dempstershafer. Pdf dempstershafer theory offers an alternative to traditional probabilistic. Dempster in the context of statistical inference, the theory was later. Dempstershafer evidence combining for antihoneypot. Exploring the combination of dempstershafer theory and. Clearly, this result is counterintuitive since it implies complete support for a diagnosis that both a and b considered highly improbable. 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. The dempstershafer theory of evidence is a powerful method for combining measures of evidence from different classifiers. This paper presents an event recognition framework, based on dempstershafer theory, that combines evidence of events from lowlevel computer vision analytics. 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.