Learning to Predict One or More Ranks in Ordinal Regression Tasks

Authors:
Jaime Alonso;Juan José Coz;Jorge Díez;Oscar Luaces;Antonio Bahamonde
Affiliations:
Artificial Intelligence Center., University of Oviedo at Gijón, Asturias, Spain;Artificial Intelligence Center., University of Oviedo at Gijón, Asturias, Spain;Artificial Intelligence Center., University of Oviedo at Gijón, Asturias, Spain;Artificial Intelligence Center., University of Oviedo at Gijón, Asturias, Spain;Artificial Intelligence Center., University of Oviedo at Gijón, Asturias, Spain
Venue:
ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
Year:
2008

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Abstract

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 SVMand other based on Gaussian processes designed to minimize the linear loss in ordinal regression tasks.