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Title Efficient algorithms using subiterative convergence for Kemeny ranking problem
 
Names Badal, Prakash S
Das, Ashish
Date Issued 2017-10-24 (iso8601)
Abstract Multidimensional ranking is useful to practitioners in political science,
computer science, social science, medical science, and allied fields. The
objective is to identify a consensus ranking of n objects that best fi ts independent rankings given by k different judges. The Kemeny distance is
used as a metric to obtain consensus ranking. For large n, under present
computing powers, it is not feasible to identify a consensus ranking. To
address the problem, researchers have proposed several algorithms. These
algorithms are able to handle datasets with n up to 200 in a reasonable
amount of time. However, run-time increases very quickly as n increases. In
the present paper, we propose two basic algorithms - Subiterative Convergence and Greedy Algorithm. Using these basic algorithms, two advanced
algorithms - FUR and SIgFUR are developed. We show that our results are
superior both in terms of Kemeny distance, as a performance measure, and
run-time to existing algorithms. The proposed algorithms, even for large n,
runs in few minutes.
Genre Technical Report
Topic Consensus ranking
Identifier http://localhost:8080/xmlui/handle/100/18434