Hierarchical Classification Fusion framework

This paper presents a novel hierarchical Classification Fusion (CF) framework which operates on Abstract and Measurement levels simultaneously and thus exploits information patterns resulting from the output labels and posterior beliefs of individual classifiers. Furthermore the proposed classification fusion methodology allows for the decomposition of the input data, which is used to design individual classifiers, into subsets. This in turn permits individual classifiers to be re-designed per subset and in a manner that increases overall system classification performance. Experimental results are presented which demonstrate the potential of the proposed methodology in the case of multi-modal, multi-feature binary data classification problems. In addition the proposed CF design framework can be applied to multi class problems and is independent of the type of classifiers employed in the system.