Results 1  10
of
29
Clusterbased specification techniques in DempsterShafer theory
 SYMBOLIC AND QUANTITATIVE APPROACHES TO REASONING AND UNCERTAINTY, C. FROIDEVAUX, AND J. KOLAS, EDS., PROCEEDINGS OF THE EUROPEAN CONFERENCE (ECSQARU'95)
, 1995
"... When reasoning with uncertainty there are many situations where evidences are not only uncertain but their propositions may also be weakly specified in the sense that it may not be certain to which event a proposition is referring. It is then crucial not to combine such evidences in the mistaken bel ..."
Abstract

Cited by 25 (12 self)
 Add to MetaCart
(Show Context)
When reasoning with uncertainty there are many situations where evidences are not only uncertain but their propositions may also be weakly specified in the sense that it may not be certain to which event a proposition is referring. It is then crucial not to combine such evidences in the mistaken belief that they are referring to the same event. This situation would become manageable if the evidences could be clustered into subsets representing events that should be handled separately. In an earlier article we established within DempsterShafer theory a criterion function called the metaconflict function. With this criterion we can partition a set of evidences into subsets. Each subset representing a separate event. In this article we will not only find the most plausible subset for each piece of evidence, we will also find the plausibility for every subset that the evidence belongs to the subset. Also, when the number of subsets are uncertain we aim to find a posterior probability distribution regarding the number of subsets.
Clustering belief functions based on attracting and conflicting metalevel evidence
 Intelligent Systems for Information Processing: From Representation to Applications, B. BouchonMeunier, L. Foulloy and R.R. Yager (Eds.)
, 2003
"... In this paper we develop a method for clustering belief functions based on attracting and conflicting metalevel evidence. Such clustering is done when the belief functions concern multiple events, and all belief functions are mixed up. The clustering process is used as the means for separating the b ..."
Abstract

Cited by 25 (16 self)
 Add to MetaCart
In this paper we develop a method for clustering belief functions based on attracting and conflicting metalevel evidence. Such clustering is done when the belief functions concern multiple events, and all belief functions are mixed up. The clustering process is used as the means for separating the belief functions into subsets that should be handled independently. While the conflicting metalevel evidence is generated internally from pairwise conflicts of all belief functions, the attracting metalevel evidence is assumed given by some external source.
DempsterShafer clustering using Potts spin mean field theory
 Soft Computing
, 2001
"... In this article we investigate a problem within DempsterShafer theory where 2^q  1 pieces of evidence are clustered into q clusters by minimizing a metaconflict function, or equivalently, by minimizing the sum of weight of conflict over all dusters. Previously one of us developed a method based on ..."
Abstract

Cited by 18 (14 self)
 Add to MetaCart
In this article we investigate a problem within DempsterShafer theory where 2^q  1 pieces of evidence are clustered into q clusters by minimizing a metaconflict function, or equivalently, by minimizing the sum of weight of conflict over all dusters. Previously one of us developed a method based on a Hopfield and Tank model. However, for very large problems we need a method with lower computational complexity. We demonstrate that the weight of conflict of evidence can, as an approximation, be linearized and mapped to an antiferromagnetic Potts spin model. This facilitates efficient numerical solution, even for large problem sizes. Optimal or nearly optimal solutions are found for DempsterShafer clustering benchmark tests with a time complexity of approximately O(N^2 log^2 N). Furthermore, an isomorphism between the antiferromagnetic Potts spin model and a graph optimization problem is shown. The graph model has dynamic variables living on the links, which have a priori probabilities that are directly related to the pairwise conflict between pieces of evidence. Hence, the relations between three different models are shown.
Conflictbased Force Aggregation
 Proceedings of the Sixth International Command and Control Research and Technology Symposium (6th ICCRTS)
, 2001
"... In this paper we present an application where we put together two methods for clustering and classification into a force aggregation method. Both methods are based on conflicts between elements. These methods work with different type of elements (intelligence reports, vehicles, military units) on di ..."
Abstract

Cited by 18 (7 self)
 Add to MetaCart
In this paper we present an application where we put together two methods for clustering and classification into a force aggregation method. Both methods are based on conflicts between elements. These methods work with different type of elements (intelligence reports, vehicles, military units) on different hierarchical levels using specific conflict assessment methods on each level. We use DempsterShafer theory for conflict calculation between elements, DempsterShafer clustering for clustering these elements, and templates for classification. The result of these processes is a complete force aggregation on all levels handled.
Conflict Management in DempsterShafer Theory by Sequential Discounting Using the Degree of Falsity
, 2008
"... In this paper we develop a method for conflict management within DempsterShafer theory. The idea is that each piece of evidence is discounted in proportion to the degree that it contributes to the conflict. This way the contributors of conflict are managed on a case−by−case basis in relation to the ..."
Abstract

Cited by 16 (5 self)
 Add to MetaCart
In this paper we develop a method for conflict management within DempsterShafer theory. The idea is that each piece of evidence is discounted in proportion to the degree that it contributes to the conflict. This way the contributors of conflict are managed on a case−by−case basis in relation to the problem they cause. Discounting is performed in a sequence of incremental steps, with conflict updated at each step, until the overall conflict is brought down exactly to a predefined acceptable level.
Dempster's rule for evidence ordered in a complete directed acyclic graph
 International Journal of Approximate Reasoning
, 1993
"... For the case of evidence ordered in a complete directed acyclic graph this paper presents a new algorithm with lower computational complexity for Dempster's rule than that of stepbystep application of Dempster's rule. In this problem, every original pair of evidences, has a corresponding ..."
Abstract

Cited by 15 (7 self)
 Add to MetaCart
(Show Context)
For the case of evidence ordered in a complete directed acyclic graph this paper presents a new algorithm with lower computational complexity for Dempster's rule than that of stepbystep application of Dempster's rule. In this problem, every original pair of evidences, has a corresponding evidence against the simultaneous belief in both propositions. In this case, it is uncertain whether the propositions of any two evidences are in logical conflict. The original evidences are associated with the vertices and the additional evidences are associated with the edges. The original evidences are ordered, i.e., for every pair of evidences it is determinable which of the two evidences is the earlier one. We are interested in finding the most probable completely specified path through the graph, where transitions are possible only from lower to higherranked vertices. The path is here a representation for a sequence of states, for instance a sequence of snapshots of a physical object's track. A completely specified path means that the path includes no other vertices than those stated in the path representation, as opposed to an incompletely specified path that may also include other vertices than those stated. In a hierarchical network of all subsets of the frame, i.e., of all incompletely specified paths, the original and additional evidences support subsets that are not disjoint, thus it is not possible to prune the network to a tree. Instead of propagating belief, the new algorithm reasons about the logical conditions of a completely specified path through the graph. The new algorithm is O(Θ log Θ), compared to O(Θ^log Θ) of the classic brute force algorithm. After a detailed presentation of the reasoning behind the new algorithm we conclude that it is feasible to reason without approximation about completely specified paths through a complete directed acyclic graph.
Fast DempsterShafer clustering using a neural network structure
 Proceedings of the Seventh International Conference on Information Processing and Management of Uncertainty in Knowledgebased Systems (IPMU´'98)
, 1998
"... In this paper we study a problem within DempsterShafer theory where 2^n − 1 pieces of evidence are clustered by a neural structure into n clusters. The clustering is done by minimizing a metaconflict function. Previously we developed a method based on iterative optimization. However, for large scal ..."
Abstract

Cited by 12 (10 self)
 Add to MetaCart
(Show Context)
In this paper we study a problem within DempsterShafer theory where 2^n − 1 pieces of evidence are clustered by a neural structure into n clusters. The clustering is done by minimizing a metaconflict function. Previously we developed a method based on iterative optimization. However, for large scale problems we need a method with lower computational complexity. The neural structure was found to be effective and much faster than iterative optimization for larger problems. While the growth in metaconflict was faster for the neural structure compared with iterative optimization in medium sized problems, the metaconflict per cluster and evidence was moderate. The neural structure was able to find a global minimum over ten runs for problem sizes up to six clusters.
Applying data mining and machine learning techniques to submarine intelligence analysis
 Proceedings of the Third International Conference on Knowledge Discovery and Data Mining (KDD'97)
, 1997
"... We describe how specialized database technology and data analysis methods were applied by the Swedish defense to help deal with the violation of Swedish marine territory by foreign submarine intruders during the Eighties and early Nineties. Among several approaches tried some yielded interesting inf ..."
Abstract

Cited by 12 (6 self)
 Add to MetaCart
(Show Context)
We describe how specialized database technology and data analysis methods were applied by the Swedish defense to help deal with the violation of Swedish marine territory by foreign submarine intruders during the Eighties and early Nineties. Among several approaches tried some yielded interesting information, although most of the key questions remain unanswered. We conclude with a survey of belieffunction and geneticalgorithmbased methods which were proposed to support interpretation of intelligence reports and prediction of future submarine positions, respectively.
Finding a posterior domain probability distribution by specifying nonspecific evidence
 International Journal of Uncertainty, Fuzziness and KnowledgeBased Systems
, 1995
"... This article is an extension of the results of two earlier articles. In [J. Schubert, "On nonspecific evidence", Int. J. Intell. Syst. 8 (1993) 711725] we established within DempsterShafer theory a criterion function called the metaconflict function. With this criterion we can partition ..."
Abstract

Cited by 11 (11 self)
 Add to MetaCart
This article is an extension of the results of two earlier articles. In [J. Schubert, "On nonspecific evidence", Int. J. Intell. Syst. 8 (1993) 711725] we established within DempsterShafer theory a criterion function called the metaconflict function. With this criterion we can partition into subsets a set of several pieces of evidence with propositions that are weakly specified in the sense that it may be uncertain to which event a proposition is referring. In a second article [J. Schubert, "Specifying nonspecific evidence", in "Clusterbased specification techniques in DempsterShafer theory for an evidential intelligence analysis of multiple target tracks", Ph.D. Thesis, TRITANA9410, Royal Institute of Technology, Stockholm, 1994, ISBN 9171708014] we not only found the most plausible subset for each piece of evidence, we also found the plausibility for every subset that this piece of evidence belongs to the subset. In this article we aim to find a posterior probability distribution regarding the number of subsets. We use the idea that each piece of evidence in a subset supports the existence of that subset to the degree that this piece of evidence supports anything at all. From this we can derive a bpa that is concerned with the question of how many subsets we have. That bpa can then be combined with a given prior domain probability distribution in order to obtain the soughtafter posterior domain distribution.
Sequential clustering with particle filters: Extimating the number of clusters from data
 7th International Conference on Information Fusion (FUSION
, 2005
"... Abstract In this paper we develop a particle filtering approach for grouping observations into an unspecified number of clusters. Each cluster corresponds to a potential target from which the observations originate. A potential clustering with a specified number of clusters is represented by an ass ..."
Abstract

Cited by 11 (6 self)
 Add to MetaCart
(Show Context)
Abstract In this paper we develop a particle filtering approach for grouping observations into an unspecified number of clusters. Each cluster corresponds to a potential target from which the observations originate. A potential clustering with a specified number of clusters is represented by an association hypothesis. Whenever a new report arrives, a posterior distribution over all hypotheses is iteratively calculated from a prior distribution, an update model and a likelihood function. The update model is based on an association probability for clusters given the probability of false detection and a derived probability of an unobserved target. The likelihood of each hypothesis is derived from a cost value of associating the current report with its corresponding cluster according to the hypothesis. A set of hypotheses is maintained by Monte Carlo sampling. In this case, the statespace, i.e., the space of all hypotheses, is discrete with a linearly growing dimensionality over time. To lower the complexity further, hypotheses are combined if their clusters are close to each other in the observation space. Finally, for each timestep, the posterior distribution is projected into a distribution over the number of clusters. Compared to earlier information theoretic approaches for finding the number of clusters this approach does not require a large number of trial clusterings, since it maintains an estimate of the number of clusters along with the cluster configuration.