从多体比较中有效推断排名
Jack Yeung1, Daniel Kaiser1, Filippo Radicchi1
1Indiana University, Center for Complex Networks and Systems Research, Luddy School of Informatics, Computing, and Engineering, Bloomington, Indiana 47408, USA.
Physical review. E
|August 19, 2025
概括
普莱克特-卢斯 (PL) 模型可以在复杂的竞争中对实体进行排名. 一个改进的算法加快了PL排名的速度,超过了传统的对比比较,以获得更好的预测准确度.
科学领域:
- 计算统计的计算统计.
- 机器学习 机器学习
- 运动分析 运动分析
背景情况:
- 许多性能预测模型都假定对对进行比较.
- 现实世界的比赛往往涉及多个实体每比较 (例如,锦标赛,游戏).
- 普莱克特-卢斯 (PL) 模型处理多体比较,但面临着计算方面的挑战.
研究的目的:
- 为多体比较提供一个有效的Plackett-Luce模型的实现.
- 在合成和现实数据上验证改进模型的性能.
- 为了比较多体PL模型的预测能力与预测的双向模型.
主要方法:
- 开发了一种替代的,更快的算法来计算Plackett-Luce排名.
- 使用模拟和实际数据集验证了算法.
- 进行交叉验证,以评估未观察到的比较的预测准确性.
主要成果:
- 新的实现显著加快了Plackett-Luce排名的计算速度.
- 在多体数据上训练的Plackett-Luce模型显示出卓越的预测能力.
- 多体比较数据导致比预测的对数据更准确的预测.
结论:
- 高效的普莱克特-卢斯建模对于分析复杂的竞争系统至关重要.
- 对多体比较的计算直接提高了预测准确性.
- 开发的方法增强了PL模型对更大规模问题的适用性.
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