A DSmT based combination scheme for multi-class classification

This paper presents a new combination scheme for reducing the number of focal elements to manipulate in order to reduce the complexity of the combination process in the multi-class framework. The basic idea consists in using of p sources of information involved in the global scheme providing p kinds of complementary information to feed each set of p one class support vector machine classifiers independently of each other, which are designed for detecting the outliers of the same target class, then, the outputs issued from this set of classifiers are combined through the plausible and paradoxical reasoning theory for each target class. The main objective of this approach is to render calibrated outputs even when less complementary responses are encountered. An inspired version of Appriou's model for estimating the generalized basic belief assignments is presented in this paper. The proposed methodology allows decomposing a n-class problem into a series of n-combination, while providing n-calibrated outputs into the multi-class framework. The effectiveness of the proposed combination scheme with proportional conflict redistribution algorithm is validated on digit recognition application and is compared with existing statistical, learning, and evidence theory based combination algorithms.