The label ranking problem is a supervised learning scenario in which the learner predicts a total order of the class labels for a given input instance. Recently, research has increasingly focused on the partial label ranking problem, a generalization of the label ranking problem that allows ties in the predicted orders. So far, most existing learning approaches for the partial label ranking problem rely on approximation algorithms for rank aggregation in the final prediction step. This paper explores several alternative aggregation methods for this critical step, including scoring-based and probabilistic-based rank aggregation approaches. To enhance their suitability for the more general partial label ranking problem, the investigated methods are extended to increase the likelihood of producing ties. Experimental evaluations on standard benchmarks demonstrate that scoring-based variants consistently outperform the current state-of-the-art method in handling incomplete information. In contrast, probabilistic-based variants fail to achieve competitive performance.

翻译：标签排序问题是一种监督学习场景，其中学习器为给定的输入实例预测类别标签的全序排列。近年来，研究日益关注部分标签排序问题，这是标签排序问题的泛化形式，允许在预测的排序中出现并列关系。迄今为止，现有的大部分针对部分标签排序问题的学习方法在最终预测步骤中依赖于排序聚合的近似算法。本文探讨了该关键步骤的几种替代聚合方法，包括基于评分和基于概率的排序聚合方法。为了增强这些方法对更一般的部分标签排序问题的适用性，对所研究的方法进行了扩展，以提高产生并列关系的可能性。在标准基准上的实验评估表明，基于评分的方法变体在处理不完整信息方面始终优于当前最先进的方法。相比之下，基于概率的方法变体未能达到有竞争力的性能。