Ordinal Classification with Decision Rules

We consider the problem of ordinal classification, in which a value set of the decision attribute (output, dependent variable) is finite and ordered. This problem shares some characteristics of multi-class classification and regression, however, in contrast to the former, the order between class labels cannot be neglected, and, in the contrast to the latter, the scale of the decision attribute is not cardinal. In the paper, following the theoretical framework for ordinal classification, we introduce two algorithms based on gradient descent approach for learning ensemble of base classifiers being decision rules. The learning is performed by greedy minimization of so-called threshold loss, using a forward stagewise additive modeling. Experimental results are given that demonstrate the usefulness of the approach.