1. A multi-class identifier identifying a kind of characteristic data expressing predetermined feature by a first class identifier, and identifying in detail with respect to a specified kind of a group including specified kinds by a second class identifier, the multi-class identifier comprising: a first learner receiving the characteristic data for learning that includes a class label corresponding to any of a plurality of kinds, and generating kind identifiers based on the received characteristic data that individually identifies the kind of the characteristic data; an identification fault counter providing the characteristic data for test that includes any of class labels to the kind identifiers so that the kind identifiers individually identify the kind of the provided characteristic data, and counting, for a combination of arbitrary number of kinds among the plurality of kinds, the number of times of incorrect determination in the arbitrary number of kinds that belongs to the combination; a grouping processor, for a group of the combination for which count result is equal to or greater than a predetermined threshold, adding a group label corresponding to the group to the characteristic data for learning that includes the class label corresponding to any of the arbitrary number of kinds that belongs to the group; and a second learner receiving the characteristic data for learning including the group label, generating group identifier based on the received characteristic data that identifies the group of the characteristic data, forming the first class identifier by combining the group identifier and the kind identifier that identifies the kind being excluded from the group, and generating intra-group identifier that identifies the arbitrary number of kinds of characteristic data belonging to the group, to form the second class identifier.