A simple approach to ordinal classification

Abstract

Machine learning methods for classification problems commonly assume that the class values are unordered. However, in many practical applications, the class values do exhibit a natural order—for example, when learning how to grade. The standard approach to ordinal classification converts the class value into a numeric quantity and applies a regression learner to the transformed data, translating the output back into a discrete class value in a post-processing step. A disadvantage of this method is that it can only be applied in conjunction with a regression scheme.

In this paper, we present a simple method that enables standard classification algorithms to make use of ordering information in class attributes. By applying it in conjunction with a decision tree learner, we show that it outperforms the naive approach, which treats the class values as an unordered set. Compared to special-purpose algorithms for ordinal classification, our method has the advantage that it can be applied without any modification to the underlying learning scheme.