Current prospects on ordinal and monotonic classification

Active learning for ordinal classification based on expected cost minimization

To date, a large number of active learning algorithms have been proposed, but active learning methods for ordinal classification are under-researched. For ordinal classification, there is a total ordering among the data classes, and it is natural that the cost of misclassifying an instance as an adjacent class should be lower than that of misclassifying it as a more disparate class. However, existing active learning algorithms typically do not consider the above ordering information in query selection. Thus, most of them do not perform satisfactorily in ordinal classification. This study proposes an active learning method for ordinal classification by considering the ordering information among classes. We design an expected cost minimization criterion that imbues the ordering information. Meanwhile, we incorporate it with an uncertainty sampling criterion to impose the query instance more informative. Furthermore, we introduce a candidate subset selection method based on the k-means algorithm to reduce the computational overhead led by the calculation of expected cost. Extensive experiments on nine public ordinal classification datasets demonstrate that the proposed method outperforms several baseline methods.