On the General Application of the Tomographic Classifier Fusion Methodology

Abstract

We have previously (MCS2001) presented a mathematical metaphor setting out an equivalence between multiple expert fusion and the process of tomographic reconstruction familiar from medical imaging. However, the discussion took place only in relation to a restricted case: namely, classifiers containing discrete feature sets. This, its sequel paper, will therefore endeavour to extend the methodology to the fully general case.

The investigation is thus conducted initially within the context of classical feature selection (that is, selection algorithms that place no restriction upon the overlap of feature sets), the findings in relation to which demonstrating the necessity of a re-evaluation of the role of feature-selection when conducted within an explicitly combinatorial framework. When fully enunciated, the resulting investigation leads naturally to a completely generalised, morphologically-optimal strategy for classifier combination.