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.

翻译：现有多标签框架仅利用从标签二分（正负标签集）中推断出的信息。因此，它们未能受益于正标签之间的排序顺序，而这一概念正是本文引入的。我们提出了一种新颖的多标签排序方法：GaussianMLR，旨在学习决定正标签排序的隐式类别重要性值，而非将其视为同等重要。该方法通过统一多标签排序中排序与分类任务的策略实现。由于公开数据集的稀缺性，我们引入了八个在不同重要性因子下生成的合成数据集，为研究提供丰富且可控的实验环境。在真实与合成数据集上，我们与相关基线进行了广泛比较，并针对两个子任务分别评估性能。实验表明，我们的方法能够准确学习所融入的正标签排序表示，该表示不仅与真实情况一致，还与底层信息成比例。我们通过全面的实验研究进一步增强了这一论断的实证基础。代码开源于 https://github.com/MrGranddy/GaussianMLR。