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dc.contributor.authorFernández-Lozano, Carlos
dc.contributor.authorGestal, Marcos
dc.contributor.authorMunteanu, Cristian-Robert
dc.contributor.authorDorado, Julián
dc.contributor.authorPazos, A.
dc.date.accessioned2017-01-18T12:03:18Z
dc.date.available2017-01-18T12:03:18Z
dc.date.issued2016-12-01
dc.identifier.citationFernández-Lozano C, Gestal M, Munteanu CR, Dorado J, Pazos A. A methodology for the design of experiments in computational intelligence with multiple regression models. PeerJ [Internet]. 2016;4:e2721es_ES
dc.identifier.issn2167-8359
dc.identifier.urihttp://hdl.handle.net/2183/17976
dc.description.abstract[Abstract] The design of experiments and the validation of the results achieved with them are vital in any research study. This paper focuses on the use of different Machine Learning approaches for regression tasks in the field of Computational Intelligence and especially on a correct comparison between the different results provided for different methods, as those techniques are complex systems that require further study to be fully understood. A methodology commonly accepted in Computational intelligence is implemented in an R package called RRegrs. This package includes ten simple and complex regression models to carry out predictive modeling using Machine Learning and well-known regression algorithms. The framework for experimental design presented herein is evaluated and validated against RRegrs. Our results are different for three out of five state-of-the-art simple datasets and it can be stated that the selection of the best model according to our proposal is statistically significant and relevant. It is of relevance to use a statistical approach to indicate whether the differences are statistically significant using this kind of algorithms. Furthermore, our results with three real complex datasets report different best models than with the previously published methodology. Our final goal is to provide a complete methodology for the use of different steps in order to compare the results obtained in Computational Intelligence problems, as well as from other fields, such as for bioinformatics, cheminformatics, etc., given that our proposal is open and modifiable.es_ES
dc.description.sponsorshipGalicia. Consellería de Educación, Cultura e Educación Universitaria; R2014/025es_ES
dc.description.sponsorshipGalicia. Consellería de Educación, Cultura e Educación Universitaria; GRC2014/049es_ES
dc.description.sponsorshipInstituto de Salud Carlos III; PI13/00280es_ES
dc.language.isoenges_ES
dc.publisherPeer Jes_ES
dc.relation.urihttps://doi.org/10.7717/peerj.2721es_ES
dc.rightsAtribución 3.0 Españaes_ES
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subjectComputational intelligencees_ES
dc.subjectMachine learninges_ES
dc.subjectMethodologyes_ES
dc.subjectStatistical analysises_ES
dc.subjectExperimental designes_ES
dc.subjectRRregrses_ES
dc.titleA methodology for the design of experiments in computational intelligence with multiple regression modelses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessinfo:eu-repo/semantics/openAccesses_ES
UDC.journalTitlePeer Jes_ES


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