  Many classification applications require accurate probability estimates in
addition to good class separation but often classifiers are designed focusing
only on the latter. Calibration is the process of improving probability
estimates by post-processing but commonly used calibration algorithms work
poorly on small data sets and assume the classification task to be binary. Both
of these restrictions limit their real-world applicability. Previously
introduced Data Generation and Grouping algorithm alleviates the problem posed
by small data sets and in this article, we will demonstrate that its
application to multi-class problems is also possible which solves the other
limitation. Our experiments show that calibration error can be decreased using
the proposed approach and the additional computational cost is acceptable.
