Ordinal regression problems are those machine learning problems where the objective is to
classify patterns using a categorical scale which shows a natural order between the labels …

Nondeterministic classifiers are defined as those allowed to predict more than one class for
some entries from an input space. Given that the true class should be included in predictions …

In this paper we present a function to predict the carcass weight for beef cattle. The function
uses a few zoometric measurements of the animals taken days before the slaughter. For this …

In multilabel classification tasks the aim is to find hypotheses able to predict, for each
instance, a set of classes or labels rather than a single one. Some state-of-the-art multilabel …

The benefits of livestock breeders are usually closely related to the weight of their animals.
In this paper we present a method to anticipate the weight of each animal provided we know …

Ordinal regression (OR) is a paradigm which learns a prediction model on the data with
ordered classes. Despite much progress in OR, the existing OR works learn the classifier …

Indeterminate classifiers are cautious models able to predict more than one class in case of
high uncertainty. A problem that arises when using such classifiers is how to evaluate their …

Motivated by an agriculture case study, we discuss how to learn functions able to predict
whether the value of a continuous target variable will be greater than a given threshold. In …

In some applications of machine learning and information retrieval (eg medical diagnosis,
image recognition, pre-classification...), it can be preferable to provide less informative but …

We present a method to formulate predictions regarding continuous variables using
regressors able to predict intervals rather than single points. They can be learned explicitly …