Search results for "weighted Kemeny distance"

showing 4 items of 4 documents

Element weighted Kemeny distance for ranking data

2021

Preference data are a particular type of ranking data that arise when several individuals express their preferences over a finite set of items. Within this framework, the main issue concerns the aggregation of the preferences to identify a compromise or a “consensus”, defined as the closest ranking (i.e. with the minimum distance or maximum correlation) to the whole set of preferences. Many approaches have been proposed, but they are not sensitive to the importance of items: i.e. changing the rank of a highly-relevant element should result in a higher penalty than changing the rank of a negligible one. The goal of this paper is to investigate the consensus between rankings taking into accou…

Weighted rank correlation coefficientweighted Kemeny distanceelement weightconsensus ranking
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Consensus measures for preference rankings with ties: an approach based on position weighted Kemeny distance

2018

Preference data are a particular type of ranking data where some subjects (voters, judges, ...) give their preferences over a set of alternatives (items). It happens, in most of the real cases, that some items receive the same preference by a judge, giving raise to a ranking with ties. The purpose of our paper is to investigate on the consensus between rankings with ties taking into account the importance of swapping elements belonging to the top (or to the bottom) of the ordering (position weights). Combining the structure of the Taux proposed by Emond and Mason and the class of weighted Kemeny-Snell distances, we propose a position weighted rank correlation coefficient to compare rankings…

Weighted rank correlation Weighted Kemeny distance Position weights
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Consensus among preference rankings: a new weighted correlation coefficient for linear and weak orderings

2021

AbstractPreference data are a particular type of ranking data where some subjects (voters, judges,...) express their preferences over a set of alternatives (items). In most real life cases, some items receive the same preference by a judge, thus giving rise to a ranking with ties. An important issue involving rankings concerns the aggregation of the preferences into a “consensus”. The purpose of this paper is to investigate the consensus between rankings with ties, taking into account the importance of swapping elements belonging to the top (or to the bottom) of the ordering (position weights). By combining the structure of $$\tau _x$$ τ x proposed by Emond and Mason (J Multi-Criteria Decis…

Statistics and ProbabilityClass (set theory)Correlation coefficientApplied Mathematics02 engineering and technologyType (model theory)01 natural sciencesComputer Science ApplicationsSet (abstract data type)010104 statistics & probabilityRankingPosition (vector)StatisticsWeighted Rank correlation coefficient Weighted Kemeny distance Position weightsTies0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processing0101 mathematicsSettore SECS-S/01 - StatisticaPreference (economics)MathematicsRank correlationAdvances in Data Analysis and Classification
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Ensemble methods for ranking data with and without position weights

2020

The main goal of this Thesis is to build suitable Ensemble Methods for ranking data with weights assigned to the items’positions, in the cases of rankings with and without ties. The Thesis begins with the definition of a new rank correlation coefficient, able to take into account the importance of items’position. Inspired by the rank correlation coefficient, τ x , proposed by Emond and Mason (2002) for unweighted rankings and the weighted Kemeny distance proposed by García-Lapresta and Pérez-Román (2010), this work proposes τ x w , a new rank correlation coefficient corresponding to the weighted Kemeny distance. The new coefficient is analized analitically and empirically and represents the main…

ranking databoostingweighted Kemeny distancebaggingSettore SECS-S/01 - Statisticalinear mixed modelensemble method
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