Aggregation of Partial Rankings - An Approach Based on the Kemeny Ranking Problem

Gonzalo Nápoles*, Zoumpoulia Dikopoulou, Elpiniki Papageorgiou, Rafael Bello, Koen Vanhoof

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review


Aggregating the preference of multiple experts is a very old problem which remains without an absolute solution. This assertion is supported by the Arrow's theorem: there is no aggregation method that simultaneously satisfies three fairness criteria (non-dictatorship, independence of irrelevant alternatives and Pareto efficiency). However, it is possible to find a solution having minimal distance to the consensus, although it involves a NP-hard problem even for only a few experts. This paper presents a model based on Ant Colony Optimization for facing this problem when input data are incomplete. It means that our model should build a complete ordering from partial rankings. Besides, we introduce a measure to determine the distance between items. It provides a more complete picture of the aggregated solution. In order to illustrate our contributions we use a real problem concerning Employer Branding issues in Belgium.
Original languageEnglish
Title of host publicationAdvances in Computational Intelligence
EditorsIgnacio Rojas, Gonzalo Joya, Andreu Catala
Place of PublicationCham
PublisherSpringer International Publishing
Number of pages13
ISBN (Print)978-3-319-19222-2
Publication statusPublished - 2015
Externally publishedYes
EventInternational Work-Conference on Artificial Neural Networks
IWANN 2015
Duration: 10 Jun 2015 → …


ConferenceInternational Work-Conference on Artificial Neural Networks
IWANN 2015
Period10/06/15 → …


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