GaussianMLR: Learning Implicit Class Significance via Calibrated Multi-Label Ranking
This addresses a limitation in multi-label learning for applications requiring label ranking, though it appears incremental as it builds on existing frameworks with a novel ranking approach.
The paper tackles the problem of multi-label ranking by proposing GaussianMLR, a method that learns implicit class significance values to rank positive labels instead of treating them equally, and shows it accurately learns representations consistent with ground truth on both real-world and synthetic datasets.
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.