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

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 misclassfied 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.