1 research outputs found
Decision Support with Belief Functions Theory for Seabed Characterization
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