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Paper abstract

Learning to Predict One or More Ranks in Ordinal Regression Tasks

Jaime Alonso - University of Oviedo at Gijon, Asturias, Spain
Juan José Del Coz - University of Oviedo at Gijon, Asturias, Spain
Jorge Díez - University of Oviedo at Gijon, Asturias, Spain
Oscar Luaces - University of Oviedo at Gijon, Asturias, Spain
Antonio Bahamonde - University of Oviedo at Gijon, Asturias, Spain

Session: Regression
Springer Link: http://dx.doi.org/10.1007/978-3-540-87479-9_21

We present nondeterministic hypotheses learned from an ordinal regression task. They try to predict the true rank for an entry, but when the classification is uncertain the hypotheses predict a set of consecutive ranks (an interval). The aim is to keep the set of ranks as small as possible, while still containing the true rank. The justification for learning such a hypothesis is based on a real world problem arisen in breeding beef cattle. After defining a family of loss functions inspired in Information Retrieval, we derive an algorithm for minimizing them. The algorithm is based on posterior probabilities of ranks given an entry. A couple of implementations are compared: one based on a multiclass SVM and other based on Gaussian processes designed to minimize the linear loss in ordinal regression tasks.