JSM 2013 Home
Online Program Home
My Program

Abstract Details

Activity Number: 611
Type: Contributed
Date/Time: Wednesday, August 7, 2013 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistical Learning and Data Mining
Abstract - #310231
Title: Statistical Consistency of Multipartite Ranking
Author(s): Yoonkyung Lee*+ and Kazuki Uematsu
Companies: The Ohio State University and Ohio State University
Keywords: Ranking ; Consistency ; Ordinal response ; Convex risk

Statistical consistency in multipartite ranking is investigated as an extension of bipartite ranking. We consider the consistency of ranking algorithms through minimization of the theoretical risk which combines pairwise ranking errors of ordinal categories. The extension shows that for a certain class of convex loss functions including exponential loss, the optimal ranking function can be represented as a ratio of weighted likelihood of upper categories to lower categories, where the weights are given by the misranking costs. This result also bridges traditional ranking methods such as proportional odds model in statistics with algorithmic ranking methods in machine learning. We illustrate our findings with simulation study and real data analysis.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2013 program

2013 JSM Online Program Home

For information, contact jsm@amstat.org or phone (888) 231-3473.

If you have questions about the Continuing Education program, please contact the Education Department.

The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.

ASA Meetings Department  •  732 North Washington Street, Alexandria, VA 22314  •  (703) 684-1221  •  meetings@amstat.org
Copyright © American Statistical Association.