比较不同排序聚合方法，提升部分标签排序的准确性。
A comparative analysis of rank aggregation methods for the partial label ranking problem
- 采用基于评分的聚合方法处理标签排序中的并列关系。
- 在标准数据集上，新方法对不完整信息的处理优于当前最佳模型。
- 适合关注排序学习与不确定性建模的研究者参考。
标签排序问题是一种监督学习任务，要求为给定输入实例预测类别标签的全序关系。近年来研究逐渐转向部分标签排序问题，该问题允许预测结果中存在并列关系。现有方法大多依赖于排序聚合的近似算法来完成最终预测。本文探讨了几种替代的聚合方法，包括基于评分和非参数概率的排序聚合方法，并对其进行了扩展以增强生成并列结果的能力。在标准基准上的实验表明，基于评分的方法在处理不完整信息时持续优于当前最先进的方法；而非参数概率方法则未能达到有竞争力的性能。
原文摘要 · Abstract (English)
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 non-parametric 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, non-parametric probabilistic-based variants fail to achieve competitive performance.
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