GaussianMLR: Learning Implicit Class Significance via Calibrated Multi-Label Ranking

Vahit Buğra Yeşilkaynak, Emine Dari, Alican Mertan, Gozde Unal

Abstract:

Existing multi-label frameworks only exploit the information deduced from the
bipartition of the labels into a positive and negative set. Therefore, they do not
benefit from the ranking order between positive labels, which is the concept we
introduce in this paper. We propose a novel multi-label ranking method: Gaus-
sianMLR, which aims to learn implicit class significance values that determine the
positive label ranks instead of treating them as of equal importance, by following
an approach that unifies ranking and classification tasks associated with multi-label
ranking. Due to the scarcity of public datasets, we introduce eight synthetic datasets
generated under varying importance factors to provide an enriched and controllable
experimental environment for this study. On both real-world and synthetic datasets,
we carry out extensive comparisons with relevant baselines and evaluate the perfor-
mance on both of the two sub-tasks. We show that our method is able to accurately
learn a representation of the incorporated positive rank order, which is not only
consistent with the ground truth but also proportional to the underlying information.
We strengthen our claims empirically by conducting comprehensive experimental
studies.