论文标题
一种监督的学习方法
A Supervised Learning Approach to Rankability
论文作者
论文摘要
数据的排名性是一个最近提出的问题，它考虑了表示为图的数据集的能力，以产生其包含的项目的有意义排名。为了研究这一概念，基于与组合和线性代数方法的完整优势图的比较，最近已经提出了许多排名措施。在本文中，我们回顾了这些措施，并突出了它们引起的一些问题，然后再提出新的方法来评估排名性，这些方法可以根据有效的估计。最后，我们通过将这些措施应用于合成和现实生活中的体育数据来比较这些措施。
The rankability of data is a recently proposed problem that considers the ability of a dataset, represented as a graph, to produce a meaningful ranking of the items it contains. To study this concept, a number of rankability measures have recently been proposed, based on comparisons to a complete dominance graph via combinatorial and linear algebraic methods. In this paper, we review these measures and highlight some questions to which they give rise before going on to propose new methods to assess rankability, which are amenable to efficient estimation. Finally, we compare these measures by applying them to both synthetic and real-life sports data.
