Combining binary machines for multi-class classification problems is a popular idea, and many related methods have been proposed. When each binary machine reports a hard decision (a binary classifier), one of the most popular methods is to use the error correcting output codes (ECOC), while when each machine reports a soft decision (a binary predictor), another interesting idea is to use the Bradley-Terry (BT) model. In this paper, these methods are reviewed from a statistical viewpoint. As a result, a common framework will be given and natural extensions are derived for both of the ECOC and the BT model approaches.