The rankability of weighted data from pairwise comparisons

Paul E. Anderson; Timothy P. Chartier; Amy N. Langville; Kathryn E. Pedings-Behling

doi:10.3934/fods.2021002

Article Contents

2021, Volume 3, Issue 1: 1-26. Doi: 10.3934/fods.2021002

This issue Previous Article Next Article Markov chain simulation for multilevel Monte Carlo

The rankability of weighted data from pairwise comparisons

1.
Department of Computer Science and Software Engineering, California Polytechnic State University, San Luis Obispo, CA, USA
2.
Department of Mathematics and Computer Science, Davidson College, Davidson, NC, USA
3.
Mathematics Department, College of Charleston, Charleston, SC, USA

^* Corresponding author: Langville
^* Corresponding author: Langville

Received: August 2020

Revised: December 2020

Early access: January 2021

Published: March 2021

Abstract Full Text(HTML) Figure(9) / Table(4) Related Papers Cited by

Abstract

In prior work [4], Anderson et al. introduced a new problem, the rankability problem, which refers to a dataset's inherent ability to produce a meaningful ranking of its items. Ranking is a fundamental data science task with numerous applications that include web search, data mining, cybersecurity, machine learning, and statistical learning theory. Yet little attention has been paid to the question of whether a dataset is suitable for ranking. As a result, when a ranking method is applied to a dataset with low rankability, the resulting ranking may not be reliable.

Rankability paper [4] and its methods studied unweighted data for which the dominance relations are binary, i.e., an item either dominates or is dominated by another item. In this paper, we extend rankability methods to weighted data for which an item may dominate another by any finite amount. We present combinatorial approaches to a weighted rankability measure and apply our new measure to several weighted datasets.

Keywords:

Mathematics Subject Classification: Primary: 90C08, 90C10, 52B12; Secondary: 90C35.

Citation:

Full Text(HTML)

Figure 1. Cityplot of $ 8 \times 8 $ data matrix with original ordering and hillside reordering

Download: Full-size image PowerPoint slide

Figure 2. Cityplots of $ n = 8 $ college football data matrices with the original ordering (left) and the optimal hillside reordering (right). The top row is the 2008 season, a less rankable season with hillside $ \delta = 155 $ and $ \rho = 6 $. The bottom row is the 2005 season, a more rankable season with hillside $ \delta = 92 $ and $ \rho = 4 $

Download: Full-size image PowerPoint slide

Figure 3. Approximate fractional matrix $ {\bf X}^*({\bf r}, {\bf r}) $ for Example 1 obtained by the Interior Point solver of the linear programming relaxation

Download: Full-size image PowerPoint slide

Figure 4. Spaghetti plots and summary of diversity of $ P $ sets for Examples 1 and 2

Download: Full-size image PowerPoint slide

Figure 5. Two maximally discordant optimal solutions for Examples 1 and 2

Download: Full-size image PowerPoint slide

Figure 6. $ X^* $ color-coded visualization of years 2009, 2013, and 2016 NCAA March Madness using the top performing LOP formulation

Download: Full-size image PowerPoint slide

Figure 7. $ X^* $ color-coded visualization of years 2009, 2013, and 2016 NCAA March Madness using the top performing hillside formulation

Download: Full-size image PowerPoint slide

Figure 8. $ X^* $ color-coded visualization for each year of NCAA March Madness using the top performing LOP formulation

Download: Full-size image PowerPoint slide

Figure 9. $ X^* $ color-coded visualization for each year of NCAA March Madness using the top performing hillside formulation

Download: Full-size image PowerPoint slide

Table 1. Sample Input/Output economic data based on Japan 2005 [22] (A) and its graphical representation in (B)

| Show Table

DownLoad: CSV

Table 2. Sample of movie rating data based on MovieLens [19] (A) and graphical representation of user rating data transformed into pairwise comparisons (B)

| Show Table

DownLoad: CSV

Table 3. Sample of NCAA Men's Basketball games is shown in (A) and the graphical representation of the aggregate dominance information, i.e., $ {\bf D} $ is shown in (B)

| Show Table

DownLoad: CSV

Table 4. 5 fold cross-validation results for predicting the upset measure for NCAA Men's March Madness 2002-2018

	Dominance Method	Parameters	Method	MAE
1	Direct+Indirect	dt=0, st=1, wi=1	Hillside	3.148221
2	Direct+Indirect	dt=0, st=0, wi=1	Hillside	3.148221
3	Direct+Indirect	dt=1, st=0, wi=1	LOP	3.172793
4	Direct+Indirect	dt=1, st=1, wi=1	LOP	3.172793
5	Direct+Indirect	dt=0, st=0, wi=0.25	Hillside	3.213978
6	Direct+Indirect	dt=0, st=1, wi=0.25	Hillside	3.213978
7	Direct	dt=2	Hillside	3.309455
8	Direct+Indirect	dt=2, st=1, wi=1	LOP	3.311588
9	Direct+Indirect	dt=2, st=0, wi=1	LOP	3.311588
10	Direct+Indirect	dt=2, st=2, wi=0.5	Hillside	3.331698

| Show Table

DownLoad: CSV

Related Papers

Cited by

References

[1]	T. Achterberg, Scip: Solving constraint integer programs, Mathematical Programming Computation, 1 (2009), 1-41. doi: 10.1007/s12532-008-0001-1.
[2]	N. Ailon, M. Charikar and A. Newman, Aggregating inconsistent information: Ranking and clustering, Journal of the ACM, 55 (2008), 27 pp. doi: 10.1145/1411509.1411513.
[3]	I. Ali, W. D. Cook and M. Kress, On the minimum violations ranking of a tournament, Management Science, 32 (1986), 660-672. doi: 10.1287/mnsc.32.6.660.
[4]	P. Anderson, T. Chartier and A. Langville, The rankability of data, SIAM Journal on the Mathematics of Data Science, (2019), 121–143. doi: 10.1137/18M1183595.
[5]	P. Anderson, T. Chartier, A. Langville and K. Pedings-Behling, IGARDS technical report, 2019. Available from: https://igards.github.io/research/IGARDS_Technical_Report_November_2019.pdf.
[6]	P. Anderson, T. Chartier, A. Langville and K. Pedings-Behling, Revisiting rankability of unweighted data technical report, 2020. Available from: https://igards.github.io/research/Revisiting_Rankability_Unweighted_Data.pdf.
[7]	R. D. Armstrong, W. D. Cook and L. M. Seiford, Priority ranking and consensus formation: The case of ties, Management Science, 28 (1982), 638-645. doi: 10.1287/mnsc.28.6.638.
[8]	J. Brenner and P. Keating, Chaos kills, ESPN, The Magazine, (2016), 20–23.
[9]	S. Brin and L. Page, The anatomy of a large-scale hypertextual web search engine, Computer Networks and ISDN Systems, 33 (1998), 107–117. doi: 10.1016/S0169-7552(98)00110-X.
[10]	S. Brin, L. Page, R. Motwami and T. Winograd, The PageRank citation ranking: Bringing order to the web, Tech. Report 1999-0120, Computer Science Department, Stanford University, 1999.
[11]	T. R. Cameron, A. N. Langville and H. C. Smith, On the graph Laplacian and the rankability of data, Linear Algebra and its Applications, 588 (2020), 81-100. doi: 10.1016/j.laa.2019.11.026.
[12]	C. R. Cassady, L. M. Maillart and S. Salman, Ranking sports teams: A customizable quadratic assignment approach, INFORMS: Interfaces, 35 (2005), 497-510. doi: 10.1287/inte.1050.0171.
[13]	T. P. Chartier, E. Kreutzer, A. N. Langville and K. E. Pedings, Sensitivity of ranking vectors, SIAM Journal on Scientific Computing, 33 (2011), 1077–1102. doi: 10.1137/090772745.
[14]	B. J. Coleman, Minimizing game score violations in college football rankings, INFORMS: Interfaces, 35 (2005), 483-496. doi: 10.1287/inte.1050.0172.
[15]	W. D. Cook and L. M. Seiford, Priority ranking and consensus formation, Management Science, 24 (1978), 1721-1732. doi: 10.1287/mnsc.24.16.1721.
[16]	T. G. Dietterich, Approximate statistical tests for comparing supervised classification learning algorithms, Neural Computation, 10 (1998), 1895-1923. doi: 10.1162/089976698300017197.
[17]	S. Dutta, S. H. Jacobson and J. J. Sauppe, Identifying ncaa tournament upsets using balance optimization subset selection, Journal of Quantitative Analysis in Sports, 13 (2017), 79-93.
[18]	M. R. Garey and D. S. Johnson, Computers and Intractability: A Guide to the Theory of NP-Completeness, Freeman, 1979.
[19]	F. M. Harper and J. A. Konstan, The movielens datasets: History and context, Acm Transactions on Interactive Intelligent Systems (TIIS), 5 (2015), 1-19. doi: 10.1145/2827872.
[20]	X. Jiang, L.-H. Lim, Y. Yao and Y. Ye, Statistical ranking and combinatorial Hodge theory, Mathematical Programming, 127 (2011), 203-244. doi: 10.1007/s10107-010-0419-x.
[21]	P. Keating, personal communication.
[22]	Y. Kondo, Triangulation of input-output tables based on mixed integer programs for inter-temporal and inter-regional comparison of production structures, Journal of Economic Structures, 3 (2014), 1-19. doi: 10.1186/2193-2409-3-2.
[23]	A. N. Langville and C. D. Meyer, Who's #1? The Science of Rating and Ranking Items, Princeton University Press, Princeton, 2012.
[24]	A. N. Langville, K. Pedings and Y. Yamamoto, A minimum violations ranking method, Optimization and Engineering, (2011), 1–22. doi: 10.1007/s11081-011-9135-5.
[25]	A. S. Lee and D. R. Shier, A method for ranking teams based on simple paths, UMAP Journal, 39 (2018), 353-371.
[26]	W. W. Leontief, Input-Output Economics, 2^nd edition, Oxford University Press, 1986. doi: 10.1038/scientificamerican1051-15.
[27]	M. J. Lopez and G. J. Matthews, Building an NCAA men's basketball predictive model and quantifying its success, Journal of Quantitative Analysis in Sports, 11 (2015), 5-12. doi: 10.1515/jqas-2014-0058.
[28]	R. Marti and G. Reinelt, The linear ordering problem: Exact and heuristic methods in combinatorial optimization, AMS, 2011. doi: 10.1007/978-3-642-16729-4.
[29]	S. Mehrotra and Y. Ye, Finding an interior point in the optimal face of linear programs, 62 (1993), 497–515. doi: 10.1007/BF01585180.
[30]	J. Park, On minimum violations ranking in paired comparisons, 2005.
[31]	G. Reinelt, The Linear Ordering Problem: Algorithms and Applications, Heldermann Verlag, 1985.
[32]	V. N. Vapnik, The Nature of Statistical Learning Theory, Springer-Verlag, Berlin, Heidelberg, 1995. doi: 10.1007/978-1-4757-2440-0.
[33]	S. Wartenberg, How many people will fill out March Madness brackets?, The Columbus Dispatch, (2015).