A highly scalable algorithm for weak rankings aggregation

The Optimal Bucket Order Problem (OBOP) is a rank aggregation problem which consists in finding a consensus ranking (with ties) that generalizes a set of input rankings. In this paper, with the aim of solving the OBOP in an efficient and scalable way, we propose several greedy algorithms based on di...

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Detalles Bibliográficos
Autores: Aledo Sánchez, Juan Ángel, Gámez Martín, José Antonio, Rosete Suárez, Alejandro
Tipo de recurso: artículo
Fecha de publicación:2021
País:España
Institución:Universidad de Castilla-La Mancha
Repositorio:RUIdeRA. Repositorio Institucional de la UCLM
OAI Identifier:oai:ruidera.uclm.es:10578/32186
Acceso en línea:http://hdl.handle.net/10578/32186
Access Level:acceso abierto
Palabra clave:Rank aggregation
Optimal bucket order problem
Partial ranking
Weak order
Hierarchical clustering
Scalability
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spelling A highly scalable algorithm for weak rankings aggregationAledo Sánchez, Juan ÁngelGámez Martín, José AntonioRosete Suárez, AlejandroRank aggregationOptimal bucket order problemPartial rankingWeak orderHierarchical clusteringScalabilityThe Optimal Bucket Order Problem (OBOP) is a rank aggregation problem which consists in finding a consensus ranking (with ties) that generalizes a set of input rankings. In this paper, with the aim of solving the OBOP in an efficient and scalable way, we propose several greedy algorithms based on different sort-first and cluster-second strategies. More specifically, the sorting step is based on the Borda method, whereas in the cluster step, pairs of adjacent buckets are suitably joined. The proposed methods are experimentally compared with the state-of-the-art greedy algorithms for solving the OBOP by using a large benchmark of real-world databases. Furthermore, we provide a complete statistical analysis of the experimental study, which shows that several of the proposed algorithms outperform the current state-of-the-art greedy algorithms. We also analyze the trade-off between accuracy and execution time of the algorithms to guide the users regarding the selection of the best option for each particular case. The study carried out shows that our proposal is not only competitive in terms of accuracy with the state-of-the-art evolutionary strategy for dealing with the OBOP, but is also fast and scalable.Elsevier202320232021info:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10578/32186reponame:RUIdeRA. Repositorio Institucional de la UCLMinstname:Universidad de Castilla-La ManchaInglésRTI2018-101867-B-I002021-GRIN-31218info:eu-repo/semantics/openAccessAttribution-NonCommercial-NoDerivs 3.0 Spainhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/oai:ruidera.uclm.es:10578/321862026-05-27T07:36:41Z
dc.title.none.fl_str_mv A highly scalable algorithm for weak rankings aggregation
title A highly scalable algorithm for weak rankings aggregation
spellingShingle A highly scalable algorithm for weak rankings aggregation
Aledo Sánchez, Juan Ángel
Rank aggregation
Optimal bucket order problem
Partial ranking
Weak order
Hierarchical clustering
Scalability
title_short A highly scalable algorithm for weak rankings aggregation
title_full A highly scalable algorithm for weak rankings aggregation
title_fullStr A highly scalable algorithm for weak rankings aggregation
title_full_unstemmed A highly scalable algorithm for weak rankings aggregation
title_sort A highly scalable algorithm for weak rankings aggregation
dc.creator.none.fl_str_mv Aledo Sánchez, Juan Ángel
Gámez Martín, José Antonio
Rosete Suárez, Alejandro
author Aledo Sánchez, Juan Ángel
author_facet Aledo Sánchez, Juan Ángel
Gámez Martín, José Antonio
Rosete Suárez, Alejandro
author_role author
author2 Gámez Martín, José Antonio
Rosete Suárez, Alejandro
author2_role author
author
dc.subject.none.fl_str_mv Rank aggregation
Optimal bucket order problem
Partial ranking
Weak order
Hierarchical clustering
Scalability
topic Rank aggregation
Optimal bucket order problem
Partial ranking
Weak order
Hierarchical clustering
Scalability
description The Optimal Bucket Order Problem (OBOP) is a rank aggregation problem which consists in finding a consensus ranking (with ties) that generalizes a set of input rankings. In this paper, with the aim of solving the OBOP in an efficient and scalable way, we propose several greedy algorithms based on different sort-first and cluster-second strategies. More specifically, the sorting step is based on the Borda method, whereas in the cluster step, pairs of adjacent buckets are suitably joined. The proposed methods are experimentally compared with the state-of-the-art greedy algorithms for solving the OBOP by using a large benchmark of real-world databases. Furthermore, we provide a complete statistical analysis of the experimental study, which shows that several of the proposed algorithms outperform the current state-of-the-art greedy algorithms. We also analyze the trade-off between accuracy and execution time of the algorithms to guide the users regarding the selection of the best option for each particular case. The study carried out shows that our proposal is not only competitive in terms of accuracy with the state-of-the-art evolutionary strategy for dealing with the OBOP, but is also fast and scalable.
publishDate 2021
dc.date.none.fl_str_mv 2021
2023
2023
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10578/32186
url http://hdl.handle.net/10578/32186
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv RTI2018-101867-B-I00
2021-GRIN-31218
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
Attribution-NonCommercial-NoDerivs 3.0 Spain
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
eu_rights_str_mv openAccess
rights_invalid_str_mv Attribution-NonCommercial-NoDerivs 3.0 Spain
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:RUIdeRA. Repositorio Institucional de la UCLM
instname:Universidad de Castilla-La Mancha
instname_str Universidad de Castilla-La Mancha
reponame_str RUIdeRA. Repositorio Institucional de la UCLM
collection RUIdeRA. Repositorio Institucional de la UCLM
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,300724

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