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Decision Support with Belief Functions Theory for Seabed Characterization

Abstract : The 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.
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https://hal.archives-ouvertes.fr/hal-00281740
Contributeur : Arnaud Martin <>
Soumis le : dimanche 25 mai 2008 - 22:18:19
Dernière modification le : vendredi 13 décembre 2019 - 10:42:05
Document(s) archivé(s) le : mardi 21 septembre 2010 - 17:00:13

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MartinFusion2008_Decision.pdf
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  • HAL Id : hal-00281740, version 2
  • ARXIV : 0805.3939

Citation

Arnaud Martin, Isabelle Quidu. Decision Support with Belief Functions Theory for Seabed Characterization. International Conference on Information Fusion, Jun 2008, Cologne, Germany. pp.81957103. ⟨hal-00281740v2⟩

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