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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 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.
Type
Working Paper
Type of thesis
Series
Computer Science Working Papers
Citation
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.
Date
2001-11-01
Publisher
University of Waikato, Department of Computer Science
Degree
Supervisors
Rights