  We propose a method for combining probabilistic outputs of classifiers to
make a single consensus class prediction when no further information about the
individual classifiers is available, beyond that they have been trained for the
same task. The lack of relevant prior information rules out typical
applications of Bayesian or Dempster-Shafer methods, and the default approach
here would be methods based on the principle of indifference, such as the sum
or product rule, which essentially weight all classifiers equally. In contrast,
our approach considers the diversity between the outputs of the various
classifiers, iteratively updating predictions based on their correspondence
with other predictions until the predictions converge to a consensus decision.
The intuition behind this approach is that classifiers trained for the same
task should typically exhibit regularities in their outputs on a new task; the
predictions of classifiers which differ significantly from those of others are
thus given less credence using our approach. The approach implicitly assumes a
symmetric loss function, in that the relative cost of various prediction errors
are not taken into account. Performance of the model is demonstrated on
different benchmark datasets. Our proposed method works well in situations
where accuracy is the performance metric; however, it does not output
calibrated probabilities, so it is not suitable in situations where such
probabilities are required for further processing.
