Despite that the majority of machine learning approaches
aim to solve binary classification problems, several real-world applica-
tions require specialized algorithms able to handle many different classes,
as in the case of single-label multi-class and multi-label classification
problems. The Label Ranking framework is a generalization of the above
mentioned settings, which aims to map instances from the input space
to a total order over the set of possible labels. However, generally these
algorithms are more complex than binary ones, and their application on
large-scale datasets could be untractable.
The main contribution of this work is the proposal of a novel general on-
line preference-based label ranking framework. The proposed framework
is able to solve binary, multi-class, multi-label and ranking problems. A
comparison with other baselines has been performed, showing effective-
ness and efficiency in a real-world large-scale multi-label task.