01930nas a2200193   4500008004100000245011400041210006900155260000900224300001400233490000600247520123200253653003201485653003201517653002101549100001401570700001301584700002201597856011701619       2007                        eng d00aInformation vs. Robustness in rank aggregation: Models, algorithms and a statistical framework for evaluation0 aInformation vs Robustness in rank aggregation Models algorithms   c2007  a292 - 3080 v53 aThe rank aggregation problem has been studied extensively in recent years with a focus on how to combine several different rankers to obtain a consensus aggregate ranker. We study the rank aggregation problem from a different perspective: how the individual input rankers impact the performance of the aggregate ranker. We develop a general statistical framework based on a model of how the individual rankers depend on the ground truth ranker. Within this framework, one can generate synthetic data sets and study the performance of different aggregation methods. The individual rankers, which are the inputs to the rank aggregation algorithm, are statistical perturbations of the ground truth ranker. With rigorous experimental evaluation, we study how noise level and the misinformation of the rankers affect the performance of the aggregate ranker. We introduce and study a novel Kendall-tau rank aggregator and a simple aggregator called PrOpt, which we compare to some other well known rank aggregation algorithms such as average, median, CombMNZ and Markov chain aggregators. Our results show that the relative performance of aggregators varies considerably depending on how the input rankers relate to the ground truth.10aKendall-tau rank aggregator10aRank aggregation algorithms10aRank aggregators1 aAdali, S.1 aHill, B.1 aMagdon-Ismail, M.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-70350655572&partnerID=40&md5=9105bd74059b6e4c04845776bb7c00d9