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 SVM and other based on Gaussian processes designed to minimize the linear loss in ordinal regression tasks.
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Alonso, J., del Coz, J.J., Díez, J., Luaces, O., Bahamonde, A. (2008). Learning to Predict One or More Ranks in Ordinal Regression Tasks. In: Daelemans, W., Goethals, B., Morik, K. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2008. Lecture Notes in Computer Science(), vol 5211. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87479-9_21
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DOI: https://doi.org/10.1007/978-3-540-87479-9_21
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