1,361,734 research outputs found
Modeling Partially Reliable Information Sources: A General Approach Based on Dempster-Shafer Theory
Get PDFCombining testimonial reports from independent and partially reliable information sources is an important problem of uncertain reasoning. Within the framework of Dempster-Shafer theory, we propose a general model of partially reliable sources which includes several previously known results as special cases. The paper reproduces these results, gives a number of new insights, and thereby contributes to a better understanding of this important application of reasoning with uncertain and incomplete information.Articl
Induced aggregation operators in decision making with the Dempster-Shafer belief structure
Get PDFWe study the induced aggregation operators. The analysis begins with a revision of some basic concepts such as the induced ordered weighted averaging (IOWA) operator and the induced ordered weighted geometric (IOWG) operator. We then analyze the problem of decision making with Dempster-Shafer theory of evidence. We suggest the use of induced aggregation operators in decision making with Dempster-Shafer theory. We focus on the aggregation step and examine some of its main properties, including the distinction between descending and ascending orders and different families of induced operators. Finally, we present an illustrative example in which the results obtained using different types of aggregation operators can be seen.aggregation operators, dempster-shafer belief structure, uncertainty, iowa operator, decision making
Equivalence of the Dempster-Shafer rule and the maximum likelihood rule implies convexity
Get PDFIt is known that if a capacity is convex then the Dempster-Shafer rule for the capacity is equivalent to the maximum likelihood rule for the core of the capacity. This paper shows that the converse is also true that is, a capacity must be convex if these two rules are equivalent.
Dempster-Shafer for Anomaly Detection
No full textIn this paper, we implement an anomaly detection system using the Dempster-Shafer method. Using two standard benchmark problems we show that by combining multiple signals it is possible to achieve better results than by using a single signal. We further show that by applying this approach to a real-world email dataset the algorithm works for email worm detection. Dempster-Shafer can be a promising method for anomaly detection problems with multiple features (data sources), and two or more classes
The Dempster-Shafer Theory
No full textThe initial work introducing Dempster-Shafer (D-S) theory is found in Dempster (1967) and Shafer (1976). Since its introduction the very name causes confusion, a more general term often used is belief functions (both used intermittently here). Nguyen (1978) points out, soon after its introduction, that the rudiments of D-S theory can be considered through distributions of random sets. More furtive comparison has been with the traditional Bayesian theory, where D-S theory has been considered a generalisation of it (Schubert, 1994). Cobb and Shenoy (2003) direct its attention to the comparison of D-S theory and the Bayesian formulisation. Their conclusions are that they have the same expressive power, but that one technique cannot simply take the role of the other. The association with artificial intelligence (AI) is clearly outlined in Smets (1990), who at the time, acknowledged the AI community has started to show interest for what they call the Dempster-Shafer model. It is of interest that even then, they highlight that there is confusion on what type of version of D-S theory is considered. D-S theory was employed in an event driven integration reasoning scheme in Xia et al. (1997), associated with automated route planning, which they view as a very important branch in applications of AI. Liu (1999) investigated Gaussian belief functions and specifically considered their proposed computation scheme and its potential usage in AI and statistics. Huang and Lees (2005) apply a D-S theory model in natural-resource classification, comparing with it with two other AI models. Wadsworth and Hall (2007) considered D-S theory in a combination with other techniques to investigate site-specific critical loads for conservation agencies. Pertinently, they outline its positioning with respect to AI (p. 400); The approach was developed in the AI (artificial intelligence) community in an attempt to develop systems that could reason in a more human manner and particularly the ability of human experts to “diagnose” situations with limited information. This statement is pertinent here, since emphasis within the examples later given is more towards the general human decision making problem and the handling of ignorance in AI. Dempster and Kong (1988) investigated how D-S theory fits in with being an artificial analogy for human reasoning under uncertainty. An example problem is considered, the murder of Mr. White, where witness evidence is used to classify the belief in the identification of an assassin from considered suspects. The numerical analyses presented exposit a role played by D-S theory, including the different ways it can act on incomplete knowledge
Using Dempster-Shafer theory in data mining
No full textThe origins of Dempster-Shafer theory (DST) go back to the work by Dempster (1967) who developed a system of upper and lower probabilities. Following this, his student Shafer (1976), in their book “A Mathematical Theory of Evidence” developed Dempster’s work, including a more thorough explanation of belief functions, a more general term for DST. In summary, it is a methodology for evidential reasoning, manipulating uncertainty and capable of representing partial knowledge (Haenni & Lehmann, 2002; Kulasekere, Premaratne, Dewasurendra, Shyu, & Bauer, 2004; Scotney & McClean, 2003). The perception of DST as a generalisation of Bayesian theory (Shafer & Pearl, 1990), identifies its subjective view, simply, the probability of an event indicates the degree to which someone believes it. This is in contrast to the alternative frequentist view, understood through the “Principle of I sufficient reasoning”, whereby in a situation of ignorance a Bayesian approach is forced to evenly allocate subjective (additive) probabilities over the frame of discernment. See Cobb and Shenoy (2003) for a contemporary comparison between Bayesian and belief function reasoning. The development of DST includes analogies to rough set theory (Wu, Leung, & Zhang, 2002) and its operation within neural and fuzzy environments (Binaghi, Gallo, & Madella, 2000; Yang, Chen, & Wu, 2003). Techniques based around belief decision trees (Elouedi, Mellouli, & Smets, 2001), multi-criteria decision making (Beynon, 2002) and non-paramnteric regression (Petit-Renaud & Denoeux, 2004), utilise DST to allow analysis in the presence of uncertainty and imprecision. This is demonstrated, in this article, with the ‘Classification and Ranking belief Simplex’ (CaRBS) technique for object classification, see Beynon (2005a). </jats:p
A Stochastic Approach to Hierarchical Planning and Scheduling
No full textThis paper surveys recent results for stochastic discrete programming models of hierarchical planning problems. Practical problems of this nature typically involve a sequence of decision over time at an increasing level of detail and with increasingly accurate information. These may be modeled by multistage stochastic programs whose lower levels (later stages) are stochastic versions of similar NP-hard deterministic combinatorial optimization problems and hence require the use of approximations and heuristics for near-optimal solution. After a brief survey of distributional assumptions on processing times under which SEPT and LEPT policies remain optimal for m-machine scheduling problems, results are presented for various 2-level scheduling problems in which the first stage concerns the acquisition (or assignment) of machines. For example, heuristics which are asymptotically optimal in expectation as the number of jobs in the system increases are analyzed for problems whose second stages are either identical or uniform m-machine scheduling problems. A 3-level location, distribution and routing model in the plane is also discussed
The Dempster–Shafer calculus for statisticians
No full textAbstractThe Dempster–Shafer (DS) theory of probabilistic reasoning is presented in terms of a semantics whereby every meaningful formal assertion is associated with a triple (p,q,r) where p is the probability “for” the assertion, q is the probability “against” the assertion, and r is the probability of “don’t know”. Arguments are presented for the necessity of “don’t know”. Elements of the calculus are sketched, including the extension of a DS model from a margin to a full state space, and DS combination of independent DS uncertainty assessments on the full space. The methodology is applied to inference and prediction from Poisson counts, including an introduction to the use of join-tree model structure to simplify and shorten computation. The relation of DS theory to statistical significance testing is elaborated, introducing along the way the new concept of “dull” null hypothesis
A learning Dempster-Shafer model for automated building detection
No full textThis paper presents a learning Dempster-Shafer model for the detection of buildings in aerial image and range data. The process of evidence assignment in the Dempster-Shafer method is implemented through membership functions in an adaptive network-based fuzzy inference system, where a back propagation learning rule is employed to tune the evidence assignment functions using training samples. The advantage of this method is that it incorporates our knowledge about various features that can be extracted from multi-source aerial data, and the evidence that these features provide for buildings and other objects in urban and suburban areas. Experimental results show that the proposed learning model improves the performance of the Dempster-Shafer classifier in detecting buildings in multi- source aerial data.Remote SensingAerospace Engineerin
A Preventative Approach to Mitigating CW Interference in GPS Receivers
Get PDFIn the global positioning system (GPS), code division multiple access (CDMA) signals are used. Because of the known spectral characteristics of the CDMA signal, continuous wave (CW) interference has a predictable effect on the different pseudo random noise (PRN) spreading codes (unique to each satellite) depending on the Doppler frequency of the signal. The Doppler frequency for each signal is also predictable once the receiver position is known. As different satellite signals have different Doppler frequencies, the effect on the signal quality is also different. In this paper first the effect is studied analytically. The concept of an `exclusion zone` is defined and analyzed for each satellite. This exclusion zone, where that satellite should not be used due to interference degradation, is shown to be predictable for each satellite as a function of time. Using this prediction, the CW interference effect on the positioning quality of the receiver can be mitigated by ignoring the affected satellites within exclusion zones when performing position evaluation. The threshold beyond which a satellite should be excluded is then derived by studying the mutual effects of the geometry and the signal quality of that satellite on the positioning quality. Receiver autonomous integrity monitoring (RAIM) uses redundancy in measurements to perform an internal consistency check to see if all of the measurements are satisfactory. In this paper this technique is also used to mitigate the effect of CW interference on the positioning accuracy. Finally it is shown that the prediction of the exclusion zone for each satellite outperforms the RAIM algorithm in mitigation the effect of the interference when 5 satellites are visible. © 2007 Springer-Verlag
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