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Communication Dans Un Congrès Année : 2006

Modeling hesitation and conflict : a belief-based approach for multi-class problems

Résumé

Support Vector Machine (SVM) is a powerful tool for binary classification. Numerous methods are known to fuse several binary SVMs into multi-class (MC) classifiers. These methods are efficient, but an accurate study of the misclassified items leads to notice two sources of mistakes: (1) the response of each classifier does not use the entire information from the SVM, and (2) the decision method does not use the entire information from the classifier responses. In this paper, we present a method which partially prevents these two losses of information by applying Belief Theories (BTs) to SVM fusion, while keeping the efficient aspect of the classical methods.
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Dates et versions

hal-00328125 , version 1 (06-05-2021)

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Thomas Burger, Oya Aran, Alice Caplier. Modeling hesitation and conflict : a belief-based approach for multi-class problems. 5th International Conference on Machine Learning and Applications (ICMLA'06), Dec 2006, Orlando, United States. ⟨10.1109/ICMLA.2006.35⟩. ⟨hal-00328125⟩

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