Sequential rank aggregation method

Yu-ling Lin, Salem Chakhar, Rui Yang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Abstract

Rank aggregation is a fundamental technique of different application domains. In this paper, we propose a new rank aggregation method. This method models the rank aggregation problem as an assignment problem and solves it by integer programming, where the objective function is set to minimize the sum of the squared Euclidean Distance between each initial ranking and the aggregated ranking. To avoid the computational limitation in working with large datasets, a sequential aggregation approach has been adopted. This approach proceeds sequentially in several steps. In each step, only two rankings are aggregated. It thus reduces the computational limitation of the proposed method. An illustration of the proposed method using datasets of green car adoption in Taiwan is presented in this paper. The results show that the proposed method can solve the rank aggregation problem effectively and efficiently.
Original languageEnglish
Title of host publication2018 IEEE International Conference on Systems, Man, and Cybernetics (IEEE SMC2018)
PublisherIEEE
Pages3194-3200
Number of pages7
ISBN (Electronic)978-1-5386-6650-0
ISBN (Print)978-1-5386-6651-7
DOIs
Publication statusPublished - 17 Jan 2019
Event2018 IEEE International Conference on Systems, Man and Cybernetics - Miyazaki, Japan
Duration: 7 Oct 201810 Oct 2018
http://www.smc2018.org/

Publication series

NameIEEE SMC Proceedings Series
PublisherIEEE
ISSN (Print)1062-922X
ISSN (Electronic)2577-1655

Conference

Conference2018 IEEE International Conference on Systems, Man and Cybernetics
Abbreviated titleSMC 2018
Country/TerritoryJapan
CityMiyazaki
Period7/10/1810/10/18
Internet address

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