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Conference Papers Year : 2020

Correction of Belief Function to Improve the Performances of a Fusion System

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Abstract

Our application concerns the fusion of classifiers for the recognition of trees from their leaves, in the framework of belief functions theory. In order to improve the rate of good classification it is necessary to correct Bayesian mass functions. This correction will be done from the meta-knowledge which is estimated from the confusion matrix. The corrected mass functions considerably improve the recognition rate based on the decisions provided by the classifiers.

Dates and versions

hal-02874647 , version 1 (19-06-2020)

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Didier Coquin, Reda Boukezzoula, Rihab Ben Ameur. Correction of Belief Function to Improve the Performances of a Fusion System. 18th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2020, Jun 2020, Lisbon, Portugal. pp.297-311, ⟨10.1007/978-3-030-50143-3_23⟩. ⟨hal-02874647⟩
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