A simple approach to ordinal classification.
Frank, E. & Hall M. (2001). A simple approach to ordinal classification. (Working paper series. University of Waikato, Department of Computer Science. No. 01/5/2001). Hamilton, New Zealand: University of Waikato.
Permanent Research Commons link: https://hdl.handle.net/10289/64
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 nature order, for example, when learning how to grade. The standard approach to ordinal classification converts the class value into 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 naïve 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.
University of Waikato, Department of Computer Science