Ranking Individuals by Group Comparisons

Abstract

This paper proposes new approaches to rank individuals from their group comparison results. Many real-world problems are of this type. For example, ranking players from team comparisons is important in some sports. In machine learning, a closely related application is classification using coding matrices.Group comparison results are usually in two types: binary indicator outcomes (wins/losses) or measured outcomes (scores). For each type of results, we propose new models for estimating individuals` abilities, and hence a ranking of individuals. The estimation is carried out by solving convex minimization problems, for which we develop easy and efficient solution procedures. Experiments on real bridge records and multi-class classification demonstrate the viability of the proposed models. [ABSTRACT FROM AUTHORCopyright of Journal of Machine Learning Research is the property of Microtome Publishing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder`s express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)