Amaximum entropy approach to multiple classifiers combination

In this paper, we present a maximum entropy (maxent) approach to the fusion of experts opinions, or classifiers outputs, problem. Themaxent approach is quite versatile and allows us to express in a clear, rigorous,way the a priori knowledge that is available on the problem. For instance, our knowledge about the reliabil-ity of the experts and the correlations between these experts can be easily inte-grated: Each piece of knowledge is expressed in the form of a linear constraint. An iterative scaling algorithm is used in order to compute the maxent solution of the problem. The maximum entropy method seeks the joint probability den-sity of a set of random variables that has maximum entropy while satisfying the constraints. It is therefore the “most honest ” characterization of our knowledge given the available facts (constraints). In the case of conflicting constraints, we propose to minimise the “lack of constraints satisfaction ” or to relax some con-straints and recompute the maximum entropy solution. The maxent fusion rule is illustrated by some simulations. 1