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In this paper, we are interested in the fusion of classifiers providing decisions which are organized in a hierarchy, i.e., for each pattern to classify, each classifier has the possibility to choose a class, a set of classes, or a reject option. We present a method to combine these decisions based on the transferable belief model (TBM), an interpretation(More)
Classifier combination constitutes an interesting approach when solving multi-class classification problems. We propose to carry out this combination in the belief functions framework. Our approach, similar to a method proposed by Hastie and Tibshirani in a probabilistic framework, is first presented. The performances obtained on various datasets are then(More)
We propose a new analysis method to deal with sensory profile data. Such data are composed of scores attributed by human experts (or judges) in order to describe a set of products according to a given sensory descriptor. All assessments are repeated, usually three times. The first step consists in extracting and encoding the relevant information of each(More)
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