Repositorio de producción científica de la Universidad de Sevilla

Improving a multi-objective evolutionary algorithm to discover quantitative association rules

 

Advanced Search
 

Show simple item record

dc.creator Martínez Ballesteros, María del Mar es
dc.creator Troncoso Lora, Alicia es
dc.creator Martínez Álvarez, Francisco es
dc.creator Riquelme Santos, José Cristóbal es
dc.date.accessioned 2016-07-15T09:30:03Z
dc.date.available 2016-07-15T09:30:03Z
dc.date.issued 2015
dc.identifier.citation Martínez Ballesteros, M.d.M., Troncoso Lora, A., Martínez Álvarez, F. y Riquelme Santos, J.C. (2015). Improving a multi-objective evolutionary algorithm to discover quantitative association rules.  Knowledge and Information Systems, Diciembre 2015
dc.identifier.issn 0219-1377 es
dc.identifier.uri http://hdl.handle.net/11441/43660
dc.description.abstract This work aims at correcting flaws existing in multi-objective evolutionary schemes to discover quantitative association rules, specifically those based on the wellknown non-dominated sorting genetic algorithm-II (NSGA-II). In particular, a methodology is proposed to find the most suitable configurations based on the set of objectives to optimize and distance measures to rank the non-dominated solutions. First, several quality measures are analyzed to select the best set of them to be optimized. Furthermore, different strate-gies are applied to replace the crowding distance used by NSGA-II to sort the solutions for each Pareto-front since such distance is not suitable for handling many-objective problems. The proposed enhancements have been integrated into the multi-objective algorithm called MOQAR. Several experiments have been carried out to assess the algorithm’s performance by using different configuration settings, and the best ones have been compared to other existing algorithms. The results obtained show a remarkable performance of MOQAR in terms of quality measures. es
dc.description.sponsorship Ministerio de Ciencia y Tecnología TIN2011-28956-C02 es
dc.description.sponsorship Ministerio de Ciencia y Tecnología TIN2014- 55894-C2-R es
dc.description.sponsorship Junta de Andalucia P12-TIC-1728 es
dc.description.sponsorship Universidad Pablo de Olavide APPB813097 es
dc.format application/pdf es
dc.language.iso eng es
dc.publisher Springer es
dc.relation.ispartof  Knowledge and Information Systems, Diciembre 2015
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 Internacional *
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/4.0/ *
dc.subject Association rules es
dc.subject Data mining es
dc.subject Evolutionary computation es
dc.subject Pareto-optimization es
dc.title Improving a multi-objective evolutionary algorithm to discover quantitative association rules es
dc.type info:eu-repo/semantics/article es
dc.type.version info:eu-repo/semantics/acceptedVersion es
dc.rights.accessrights info:eu-repo/semantics/openAccess es
dc.contributor.affiliation Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos es
dc.relation.projectID TIN2011-28956-C02 es
dc.relation.projectID TIN2014- 55894-C2-R es
dc.relation.projectID P12-TIC-1728 es
dc.relation.projectID APPB813097 es
dc.identifier.doi http://dx.doi.org/10.1007/s10115-015-0911-y es
idus.format.extent 28 es
dc.journaltitle  Knowledge and Information Systems es
dc.publication.volumen Diciembre 2015 es
dc.identifier.idus https://idus.us.es/xmlui/handle/11441/43660
Size: 515.6Kb
Format: PDF

This item appears in the following Collection(s)

Show simple item record