Learning to Classify Ordinal Data: The Data Replication Method
Jaime S. Cardoso, Joaquim F. Pinto da Costa; 8(50):1393−1429, 2007.
Abstract
Classification of ordinal data is one of the most important tasks of relation learning. This paper introduces a new machine learning paradigm specifically intended for classification problems where the classes have a natural order. The technique reduces the problem of classifying ordered classes to the standard two-class problem. The introduced method is then mapped into support vector machines and neural networks. Generalization bounds of the proposed ordinal classifier are also provided. An experimental study with artificial and real data sets, including an application to gene expression analysis, verifies the usefulness of the proposed approach.
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