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 |
dcterms.identifier | https://ror.org/03yxnpp24 | |
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 | |