Fouss, François
[UCL]
Saerens, Marco
[UCL]
In this paper,we present amaximumentropy (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 reliability
of the experts and the correlations between these experts can be easily integrated:
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 density
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 constraints
and recompute the maximum entropy solution. The maxent fusion rule
is illustrated by some simulations.
Bibliographic reference |
Fouss, François ; Saerens, Marco. A maximum entropy approach to multiple classifiers combination. IAG - LSM Working Papers ; 04/107 (2004) 15 pages |
Permanent URL |
http://hdl.handle.net/2078/18217 |