  The classification of complex data usually requires the composition of
processing steps. Here, a major challenge is the selection of optimal
algorithms for preprocessing and classification (including parameterizations).
Nowadays, parts of the optimization process are automized but expert knowledge
and manual work are still required. We present three steps to face this process
and ease the optimization. Namely, we take a theoretical view on classical
classifiers, provide an approach to interpret the classifier together with the
preprocessing, and integrate both into one framework which enables a
semiautomatic optimization of the processing chain and which interfaces
numerous algorithms.
