  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: GaussianMLR, 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 performance
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. Code is available at
https://github.com/MrGranddy/GaussianMLR.
