Application of the transferable belief model to diagnostic problems

Smets, Philippe

doi:10.1002/(sici)1098-111x(199802/03)13:2/3<127::aid-int3>3.0.co;2-1

Cited by 80 publications

(38 citation statements)

References 21 publications

(11 reference statements)

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“…This solution is a classical probability measure from which expected utilities can be computed in order to take optimal decisions. Some of its detail and justifications can be found in [25,29] …”

Section: A3 (Categorical Belief Function) a Categorical Beliefmentioning

confidence: 99%

“…The transferable belief model (TBM) is a model for the quantified representation of epistemic uncertainty and which can be an agent, an intelligent sensor, etc., and provides a highly flexible model to manage the uncertainty encountered in the multi-sensor data fusion problems. Application of the transferable belief model (TBM) to many areas has been presented in [25][26][27][28][29] including classification and target identification during recent times. And we feel it appealing when using navigation of mobile robots.…”

Section: Introductionmentioning

confidence: 99%

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Autonomous mobile robot navigation system designed in dynamic environment based on transferable belief model

et al. 2011

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Section: A3 (Categorical Belief Function) a Categorical Beliefmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Autonomous mobile robot navigation system designed in dynamic environment based on transferable belief model

et al. 2011

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“…The disjunctive rule. When we only know that at least one of the sources of information is reliable but we do not know which is reliable, then the bba representing the combined evidence satisfies (Smets 1998b):…”

Section: Combinationmentioning

confidence: 99%

Heuristic method for attribute selection from partially uncertain data using rough sets

Trabelsi¹,

Elouedi²

2010

International Journal of General Systems

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In this paper, we deal with the problem of attribute selection from partially uncertain data based on rough sets without costly calculation. The uncertainty exists in decision attributes and is represented by the transferable belief model, one interpretation of the belief function theory. To solve this problem, we propose a heuristic method for attribute selection able to extract the more relevant features needed in the classification process. The simplification of the uncertain decision table using this heuristic method yields to learn simplified and more significant belief decision rules in a quick time. The experiments show interesting results based on two evaluation criteria such as the accuracy classification and the time complexity.

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“…The conjunctive rule of combination [15] is applicable when both sources of information (of combined bba's) are fully reliable. Conjunctive rule of combination is naturally applicable to more than two bba's.…”

Section: Conjunctive Rule Of Combinationmentioning

confidence: 99%

Frequent Itemset Mining from Databases Including One Evidential Attribute

Tobji

Yaghlane²,

Mellouli³

2008

Lecture Notes in Computer Science

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Abstract. Frequent Itemset Mining (FIM) problem has been extensively tackled in the context of perfect data. However, real applications showed that data are often imperfect (incomplete and/or uncertain) which leads to the need of FIM algorithms that process imperfect databases. In this paper we propose a new algorithm for mining frequent itemsets from databases including exactly one evidential attribute. An evidential attribute is an attribute that could have uncertain values modelled via the evidence theory, i.e., a basic belief assignment. We introduce in this paper a variant of the structure Belief Itemset Tree (BIT) for mining frequent itemsets from evidential data and we lead some experiments that showed efficiency of our mining algorithm compared to the existing ones.

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Application of the transferable belief model to diagnostic problems

Cited by 80 publications

References 21 publications

Autonomous mobile robot navigation system designed in dynamic environment based on transferable belief model

Autonomous mobile robot navigation system designed in dynamic environment based on transferable belief model

Heuristic method for attribute selection from partially uncertain data using rough sets

Frequent Itemset Mining from Databases Including One Evidential Attribute

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