This package includes two approaches to multi-class cost-sensitive learning, that is, mcKLR and mckNN. The descr iption of mcKLR can be found in our TPAMI paper and CVPR'08 paper; the descr iption of mckNN can be found in our TPAMI paper. Both approaches have been applied to face recognition with success. In these papers we argue that face recognition is inherently a task involving unequal misclassification costs, and therefore we should try to minimize the costs instead of minimizing the number of mistakes, yet almost all previous face recognition research focus only on minimizing the number of mistakes! The mcKLR and mckNN approaches, however, can also be applied to other tasks which involve multi-class cost-sensitive learning.