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* Corresponding author 1 LRI - Laboratoire de Recherche en Informatique 2 Wuhan University China 3 LaBRI - Laboratoire Bordelais de Recherche en Informatique 4 IBC - Institut de Biologie Computationnelle 5 ZENITH - Scientific Data Management LIRMM - Laboratoire d-Informatique de Robotique et de Microélectronique de Montpellier, CRISAM - Inria Sophia Antipolis - Méditerranée 6 I2BC - Institut de Biologie Intégrative de la Cellule 7 DIRO - Département d-Informatique et de Recherche Opérationnelle Montreal

Abstract : The problem of aggregating multiple rankings into one consensus ranking is an active research topic especially in the database community. Various studies have implemented methods for rank aggregation and may have come up with contradicting conclusions upon which algorithms work best.Comparing such results is cumbersome, as the original studies mixed di erent approaches and used very di erent evaluation datasets and metrics. Additionally, in real applications, the rankings to be aggregated may not be permutations where elements are strictly ordered, but they may haveties where some elements are placed at the same position.However, most of the studies have not considered ties. This paper introduces the rst large scale study of algorithms for rank aggregation with ties. More precisely, i we review rank aggregation algorithms and determine whether or not they can handle ties; ii we propose the first implementation to compute the exact solution of the Rank Aggregation with ties problem; iii we evaluate algorithms for rank aggregation with ties on a very large panel of both real and carefully generated synthetic datasets; iv we provide guidance on the algorithms to be favored depending on dataset features.





Autor: Bryan Brancotte - Bo Yang - Guillaume Blin - Sarah Cohen-Boulakia - Alain Denise - Sylvie Hamel -

Fuente: https://hal.archives-ouvertes.fr/



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