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A methodology for the design of experiments in computational intelligence with multiple regression models

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 di...

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Autores principales: Fernandez-Lozano, Carlos, Gestal, Marcos, Munteanu, Cristian R., Dorado, Julian, Pazos, Alejandro
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5136129/
https://www.ncbi.nlm.nih.gov/pubmed/27920952
http://dx.doi.org/10.7717/peerj.2721
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author Fernandez-Lozano, Carlos
Gestal, Marcos
Munteanu, Cristian R.
Dorado, Julian
Pazos, Alejandro
author_facet Fernandez-Lozano, Carlos
Gestal, Marcos
Munteanu, Cristian R.
Dorado, Julian
Pazos, Alejandro
author_sort Fernandez-Lozano, Carlos
collection PubMed
description 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.
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spelling pubmed-51361292016-12-05 A methodology for the design of experiments in computational intelligence with multiple regression models Fernandez-Lozano, Carlos Gestal, Marcos Munteanu, Cristian R. Dorado, Julian Pazos, Alejandro PeerJ Bioinformatics 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. PeerJ Inc. 2016-12-01 /pmc/articles/PMC5136129/ /pubmed/27920952 http://dx.doi.org/10.7717/peerj.2721 Text en ©2016 Fernandez-Lozano et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.
spellingShingle Bioinformatics
Fernandez-Lozano, Carlos
Gestal, Marcos
Munteanu, Cristian R.
Dorado, Julian
Pazos, Alejandro
A methodology for the design of experiments in computational intelligence with multiple regression models
title A methodology for the design of experiments in computational intelligence with multiple regression models
title_full A methodology for the design of experiments in computational intelligence with multiple regression models
title_fullStr A methodology for the design of experiments in computational intelligence with multiple regression models
title_full_unstemmed A methodology for the design of experiments in computational intelligence with multiple regression models
title_short A methodology for the design of experiments in computational intelligence with multiple regression models
title_sort methodology for the design of experiments in computational intelligence with multiple regression models
topic Bioinformatics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5136129/
https://www.ncbi.nlm.nih.gov/pubmed/27920952
http://dx.doi.org/10.7717/peerj.2721
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