Error bounds of decision templates and support vector machines in decision fusion
International Journal of Knowledge Engineering and Soft Data Paradigms
Comparison of fuzzy combiner training methods
ICCCI'12 Proceedings of the 4th international conference on Computational Collective Intelligence: technologies and applications - Volume Part I
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Information fusion is drawing increasing interest in many application contexts, especially in biomedical decision making. In this work, we provide a framework for addressing the statistical performance of the decision fusion layer. The Decision Templates (DTs) fusion method is examined as a distance based combiner and statistically compared with an SVM discriminant hyper-classifier. Our aim is broader than providing experimental results on the performance of the two fusion schemes. We attempt to highlight the theoretical advantages of support vectors as multiple attractor points in a hyper-classifier’s feature space. Moreover we show that the use of SVMs in this task is an extensible framework that can be adapted to the problem formulation.