Constructing classifiers of expensive simulation-based data by sequential experimental design

Sequential experimental design for computer experiments is frequently used to construct surrogate regression models of complex blackbox simulators when evaluations are expensive. The same methodology can be used to train classifiers of labeled data which is expensive to obtain. For certain problems classification can be a more appropriate method to obtain a solution with fewer samples.