Label Ranking with Abstention: Predicting Partial Orders by Thresholding
  Probability Distributions (Extended Abstract)
  We consider an extension of the setting of label ranking, in which the
learner is allowed to make predictions in the form of partial instead of total
orders. Predictions of that kind are interpreted as a partial abstention: If
the learner is not sufficiently certain regarding the relative order of two
alternatives, it may abstain from this decision and instead declare these
alternatives as being incomparable. We propose a new method for learning to
predict partial orders that improves on an existing approach, both
theoretically and empirically. Our method is based on the idea of thresholding
the probabilities of pairwise preferences between labels as induced by a
predicted (parameterized) probability distribution on the set of all rankings.
