  Classification performance is often not uniform over the data. Some areas in
the input space are easier to classify than others. Features that hold
information about the "difficulty" of the data may be non-discriminative and
are therefore disregarded in the classification process. We propose a
meta-learning approach where performance may be improved by post-processing.
This improvement is done by establishing a dynamic threshold on the
base-classifier results. Since the base-classifier is treated as a "black box"
the method presented can be used on any state of the art classifier in order to
try an improve its performance. We focus our attention on how to better control
the true-positive/false-positive trade-off known as the ROC curve. We propose
an algorithm for the derivation of optimal thresholds by redistributing the
error depending on features that hold information about difficulty. We
demonstrate the resulting benefit on both synthetic and real-life data.
