A Supervised Learning Approach to Rankability

• URL: http://arxiv.org/abs/2203.07364v1
• Date: Mon, 14 Mar 2022 17:55:43 GMT
• Title: A Supervised Learning Approach to Rankability
• Authors: Nathan McJames, David Malone, Oliver Mason
• Abstract summary: rankability of data is a problem that considers the ability of a dataset, represented as a graph, to produce a meaningful ranking of the items it contains. We propose new methods to assess rankability, which are amenable to efficient estimation.
• Score: 0.6015898117103067
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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