Ds is a mathematical theory of evidence based on belief functions and plausible reasoning. Particularly appealing is the mathematical theory of evidence developed by arthur dempster. Dempstershafer techniques originated in the work of dempster on the use of probabilities with upper and lower bounds. 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. In this paper, we study trust and distrust prediction based on the combination of dempster shafer theory and neural network. Distributed attack prevention using dempstershafer theory of evidence aine macdermott, qi shi and kashif kifayat school of computer science, liverpool john moores university, liverpool, l3 3af a. In this paper a special case of combination of two bodies of. Download limit exceeded you have exceeded your daily download allowance.

Approximations for decision making in the dempstershafer theory of evidence mathias bauer german research center for artificial intelligence dfki stuhlsatzenhausweg 3 66123 saarbriicken, germany email. The theory came to the attention of ai researchers in the early 1980s, when they were trying to adapt probability theory to expert systems. A special feature of this theory is the possibility to characterize uncer. Indeed, probability theory may be seen as a limit of dempstershafer evidence theory when it is assumed. 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. This research focuses on dempstershafers evidence theory and the different similarity measures used to compare two separate bodies of evidence boes. Distributed attack prevention using dempstershafer theory of. The limitations of the original ds combination rule and works to eliminate them are discussed in section 4. Dst originated when shafer extended the original work of dempster in 1976 10. Support for normalized as well as unnormalized belief functions different montecarlo algorithms for combining belief functions various methods related to the generalized bayesian theorem measures of uncertainty methods for constructing belief functions from data both python 2. Combination of evidence in dempster shafer theory sandia.

Application of dempstershafer theory of evidence model to. The dempstershafer theory owes its name to work by a. Pdf updating the evidence in the dempstershafer theory. For example, the widely used example in dempster shafer evidence theory is doctor dignosis, which doctor believe 60% the patient has disease a and 40% disease b.

An important aspect of this theory is the combination of evidence obtained from. Paradox elimination in dempstershafer combination rule. Ds theory has been widely used in computer science and engi. Dempster 1968 and glenn shafer 1976, but the kind of reasoning the theory uses can be found as far back as the seventeenth century.

A knearest neighbor classification rule based on dempster. 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. Abstract the computational complexity of reasoning within the dempstershafer theory of evidence is. The following discussion within this section on dst has mostly been adopted from klir 11. A case of combination of evidence in the dempstershafer theory. Dempster in the context of statistical inference, the theory was later developed by glenn. 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.

In dempstershafer theory a numerical measure of the weight of evidence mass assignment, m is assigned to a set of hypotheses as well as individual hypotheses. 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. 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 dempster shafer theory owes its name to work by a. The dempster shafer theory of evidence here, ds theory, for brevity, sometimes called evidential reasoning cf. The dempstershafer theory, also known as the theory of belief functions, is a generalization of the bayesian theory of subjective probability. They are deployed to trap hackers in order to help security professionals capture, control, and analyze malicious internet attacks and other activities of hackers. A data fusion algorithm based on eventdriven and dempstershafer evidence theory is proposed in this paper to reduce data packet quantities and reserve energy for wireless sensor networks upon detecting abnormal data. Introduction the ability to group complex data into a finite number of classes is important in data mining, and means that more useful decisions can be made based on the available information. Approximations for decision making in the dempstershafer. 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. The dempstershafer theory of evidence accumulation is one of the main tools for combining data obtained from multiple sources. Exploring the combination of dempstershafer theory and.

Bayes theory is based on the classical ideas of probability, while dempster shafer theory is a recent attempt to allow more interpretation of what uncertainty is all about. Dempster shafer theory application to clustering mass function belief and plausibility functions connection with rough sets interpretation. Free download advances in the dempstershafer theory of. The dempstershafer theory is both a successor of fishers. A study of dempstershafers theory of evidence in comparison to classical probability combination scott j. Pdf combination of evidence in dempstershafer theory. A botnet is an army of compromised computers controlled by a bot herder and used for illicit financial gain.

This paper presents a new classifier combination technique based on the dempstershafer theory of evidence. Section 5 investigates the semigroup structure of dempster shafer and states the main results relating it to bayes. Scalable clustering using the dempstershafer theory of evidence. Dempstershafer theory dst is a rigorous decision theory approach that provides a way to generate predictions, estimate the uncertainty associated with each prediction, and combine multiple sources of evidence to obtain a weight ofevidence prediction by quantitatively accounting for the reliability of each of the sources being combined. In this report we will discuss the above two philosophies or paradigms that make up. Abstract the computational complexity of reasoning within the dempster shafer theory of evidence is. Novel data fusion algorithm based on eventdriven and.

Classic works of the dempstershafer theory of belief functions. Finally, i discuss the measure theoretic foundation that reveals the intimate connections between the dempster shafer theory and probability theory. Main advantage of this framework is the explicit representation of ignorance. An approach to the dempstershafer theory of evidence. The theory of belief functions, also referred to as evidence theory or dempstershafer theory. Application of dempstershafer theory in fault diagnosis. Dempstershafer theory, data classification, dempsters rule of combination. Whereas the bayesian theory requires probabilities for each question of interest, belief functions allow us to base degrees of belief for one question on probabilities for a. Available evidence, even if incomplete, can be used to find imprecise probabilities for events. This paper details a robust collaborative intrusion detection. Data classification using the dempstershafer method. Dempstershafer theory introduction, connections with rough. Dempstershafer theory for combining in silico evidence and.

Jeffreylike rules of conditioning for the dempstershafer. Bhattacharya p 2000 on the dempstershafer evidence theory and nonhierarchical aggregation of belief structures. A simple view of the dempstershafer theory of evidence. The dempstershafer theory of evidence jean gordon and edward h. Dst has been used effectively for combination of different information sources which provide incomplete, and complementary knowledge. In your case, it may be better to use bayesian approch instead of dempster shafer evidence theory. The objective of this paper is to describe the potential offered by the dempstershafer theory dst of evidence as a promising improvement on traditional approaches to decision analysis. Oct 17, 2014 a python library for performing calculations in the dempster shafer theory of evidence. June 7, 2016 abstract the book that launched the dempstershafer theory. Pellegrini a athe mitre corporation, burlington road, bedford, ma 01730. On the independence requirement in dempstershafer theory. In this paper, a twostage distributionfree classification strategy was. Dempstershafer theory offers an alternative to traditional probabilistic theory for the.

The dempstershafer theory of evidence is a powerful method for combining measures of evidence from different classifiers. This paper presents a new classifier combination technique based on the dempster shafer theory of evidence. An introduction to bayesian and dempstershafer data fusion. An alternative to the bayesian theory of evidence is dempstershafer theory which produces results similar to bayesian theory. The dempster shafer theory is a natural, important and useful extension of classical probability theory.

Combination of evidence in dempstershafer theory unt. Nguyen 1978 points out, soon after its introduction, t. During the past two years, in particular, the dempstershafer theory of evidence has att. Situations and evidence for identity using dempstershafer theory. A modified belief entropy in dempstershafer framework plos. By bridging fuzzy logic and probabilistic reasoning, the theory of belief. Application of dempstershafer theory of evidence model to geoelectric and hydraulic parameters for groundwater potential zonation. Pdf a case of combination of evidence in the dempster. The emergence of expert systems as one of the major areas of activity within ai has resulted in a rapid growth of interest within the ai community in issues relating to the management of uncertainty and evidential reasoning.

Evidence combination dempstershafer theory provides a method to combine the previous measures of evidence of different sources. When occurring or emerging it will support a set of propositions. An algorithm for updating the evidence in the dempstershafer theory is presented. The application of dempstershafer theory demonstrated. If an agent obtains a new piece a of evidence, he may update his belief with the. As a more flexible mathematical tool, dempstershafer theory not only combines with other mathematical frameworks 1518, but also combines with. We study here a combination rule based on dempstershafer theory of evidence 5 which can be considered an extension of bayesian probability. Dempster shafer theory and neural network are effective and efficient strategies to deal with these difficulties and challenges. The dempstershafer theory of evidence springerlink.

As a tool to manipulate an uncertain environment, dempstershafer ds evidence theory is an established system for uncertainty management 4,5. Citeseerx document details isaac councill, lee giles, pradeep teregowda. Each neighbor of a sample to be classified is considered as an item of evidence that supports certain hypotheses regarding the class membership of that pattern. The application of dempstershafer theory demonstrated with justi. 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.

The application of dempstershafer theory demonstrated with. Special issue on the dempstershafer theory of evidence. Section 4 provides a resume of some elementary facts of group and semigroup theory. A case of combination of evidence in the dempstershafer. Dempstershafer is not a method that considers the evidence hypothesis by hypothesis as bayes theorem does, rather the evidence is considered in light of the hypotheses. Honeypots are network surveillance architectures designed to resemble easytocompromise computer systems. The foundations of ds have been laid out by dempster 24, 6 and shafer 18 22. The algorithm allows to carry out the reasoning with updating the evidence in much more efficient way than using the original. In this paper, we present a novel biometric approach for online handwritten signature recognition and verification using dempstershafer theory dst. 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. A verified matlab toolbox for the dempstershafer theory. Dempstershafer theory dst is particularly efficient in combining multiple information sources providing incomplete, imprecise, biased, and conflictive knowledge. The degrees of belief of propositions are given by probabilities or beliefs. Dempster shafer theory dst is a rigorous decision theory approach that provides a way to generate predictions, estimate the uncertainty associated with each prediction, and combine multiple sources of evidence to obtain a weight of evidence prediction by quantitatively accounting for the reliability of each of the sources being combined.

Note, there is no pior data here, just numbers from human experts. In this paper, the problem of classifying an unseen pattern on the basis of its nearest neighbors in a recorded data set is addressed from the point of view of dempstershafer theory. The dempstershafer ds theory is a powerful tool for probabilistic reasoning based on a formal calculus for combining evidence. Dempster shafer theory does not require an assumption regarding the probability of the individual constituents of the set or interval. The significant innovation of this framework is that it allows for the allocation of a probability mass to sets or intervals. Dempstershafer evidence theory 1, 2 is effective in modeling and processing uncertain information of intelligent systems. This research focuses on dempstershafers evidence theory and the different similarity measures used to compare two separate bodies of evidence. Approximations for decision making in the dempster shafer theory of evidence mathias bauer german research center for artificial intelligence dfki stuhlsatzenhausweg 3 66123 saarbriicken, germany email. Dempster shafer theory and statistical inference with weak. Particularly appealing is the mathematical theory of evidence. Combination of acoustic classifiers based on dempster.

In this work, we focused on the improvement of the accuracy rate and the reliability of a hmm based handwriting recognition system, by the use of dempstershafer theory dst. Dempstershafer evidence combining for antihoneypot. The dempster shafer theory of evidence jean gordon and edward h. Combining classifiers using the dempster shafer theory of. The dempstershafer ds theory of evidence suggests a coherent approach to aggregate evidence bearing on groups of mutually exclusive hypotheses. The dempstershafer theory is a natural, important and useful extension of classical probability theory. Updating the evidence in the dempstershafer theory. The relationship among inducing factors may be dependency, independence, and conflicting. Get your kindle here, or download a free kindle reading app. Shafer s 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. In particular, it provides explicit estimations of imprecision and con. This thesis is brought to you for free and open access by the graduate school at scholar commons. Shortliffe the drawbacks of pure probabilistic methods and of the certainty factor model have led us in recent years to consider alternate approaches. Dempstershafer theory for combining in silico evidence.

A library written in java for performing calculations in the dempstershafer theory of evidence. The dempster shafer theory of evidence is a powerful method for combining measures of evidence from different classifiers. These indices are used to code the process of reasoning under. It is much newer theory, originating in the work of a. If is the basic probability assignment provided by source, the combination. The dempstershafer theory the dempstershafer theory 8 allows us to combine evidence from different experts or other sources and provides a measure of con. A dempstershafer theory based classifier combination for. A dempstershafer theory based combination of handwriting. A mathematical theory of evidence turns 40 glenn shafer. Combining classifiers using the dempster shafer theory of evidence naseem, imran on. Pdf can evidence be combined in the dempstershafer theory.

This paper outlines some of the basics of dempstershafer theory, which is a mathematical theory for combining evidence from different sources to obtain a. A reasoning model based on an extended dempster shafer theory john yen computer science division department of electrical engineering and computer sciences university of california berkeley, ca 94720 abstract the dempster shafer ds theory of evidence suggests a. Data classification using the dempstershafer method arxiv. Dempstershafer theory and statistical inference with weak beliefs ryanmartin, jianchunzhangandchuanhailiu abstract. Using a simple engineering example, i highlight sources of confusion in the dempster shafer literature, and some questions that arise in the course of applying the dempster shafer algorithm. Dempster with extensions by glen shafer in 1987 shafer, 1976. A python library for performing calculations in the dempstershafer theory of evidence. Dempster shafer theory offers an alternative to traditional probabilistic theory for the mathematical representation of uncertainty. Different than the bayes rule, dempstershafer theory represents evidence as possibilistic belief. Dempster shafer theory of evidence hidetomo ichihashi and hideo tanaka university of osaka prefecture osaka, japan abstract jeffreys rule of conditioning is a rule for changing an additive probability distribution when the human perception of new evidence is obtained.

In this work we focus on the relationship between the dempstershafer ds and bayesian evidence accumulation. Since its introduction the very name causes confusion, a more general term often used is belief functions both used intermittently here. Dempster at the workshop on theory of belief functions brest, 1 april 2010. The initial work introducing dempstershafer ds theory is found in dempster 1967 and shafer 1976. Combining classifiers using the dempster shafer theory of evidence. Mar 26, 2018 moreover, it should be remove redundant packets in order to lower the average energy consumption. An important aspect of this theory is the combination of evidence obtained from multiple sources and the modeling of conflict between them.

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