Decision Support with Belief Functions Theory for Seabed Characterization

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dc.creatorMartin, Arnaud
dc.creatorQuidu, Isabelle
dc.date2008-05-26
dc.date.accessioned2026-07-07T12:19:14Z
dc.date.available2026-07-07T12:19:14Z
dc.descriptionThe seabed characterization from sonar images is a very hard task because of the produced data and the unknown environment, even for an human expert. In this work we propose an original approach in order to combine binary classifiers arising from different kinds of strategies such as one-versus-one or one-versus-rest, usually used in the SVM-classification. The decision functions coming from these binary classifiers are interpreted in terms of belief functions in order to combine these functions with one of the numerous operators of the belief functions theory. Moreover, this interpretation of the decision function allows us to propose a process of decisions by taking into account the rejected observations too far removed from the learning data, and the imprecise decisions given in unions of classes. This new approach is illustrated and evaluated with a SVM in order to classify the different kinds of sediment on image sonar.
dc.identifierhttps://arxiv.org/abs/0805.3939
dc.identifierhttp://arxiv.org/abs/0805.3939
dc.identifierDans Proceeding of the 11th International Conference on Information Fusion - International Conference on Information Fusion, Cologne : Allemagne (2008)
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/212682
dc.subjectArtificial Intelligence
dc.subjectInformation Theory
dc.subjectI.4; I.5
dc.titleDecision Support with Belief Functions Theory for Seabed Characterization
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