Bagging, Boosting and Ordinal Classification

Abstract

Since the introduction of bagging and boosting many new techniques have been developed within the field of classification via aggregation methods. Most of them have in common that the class indicator is treated as a nominal response without any structure. Since in many practical situations the class must be considered as an ordered categorical variable, it seems worthwhile to take this additional information into account. We propose several variants of bagging and boosting, which make use of the ordinal structure and it is shown how the predictive power might be improved. Comparisons are based not only on misclassification rates but also on general distance measures, which reflect the difference between true and predicted class.