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

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          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.

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          Most cited references33

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          Properties of Sufficiency and Statistical Tests

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            The Collinearity Problem in Linear Regression. The Partial Least Squares (PLS) Approach to Generalized Inverses

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              Do not log-transform count data

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                Author and article information

                Contributors
                Journal
                PeerJ
                PeerJ
                peerj
                peerj
                PeerJ
                PeerJ Inc. (San Francisco, USA )
                2167-8359
                1 December 2016
                2016
                : 4
                : e2721
                Affiliations
                [1 ]Information and Communications Technologies Department, University of A Coruna , A Coruña, Spain
                [2 ]Complexo Hospitalario Universitario de A Coruña (CHUAC), Instituto de Investigacion Biomedica de A Coruña (INIBIC) , A Coruña, Spain
                Article
                2721
                10.7717/peerj.2721
                5136129
                27920952
                d739cb6a-4521-4a0e-8a83-70fb34893dc9
                ©2016 Fernandez-Lozano et al.

                This is an open access article distributed under the terms of the Creative Commons Attribution License, 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.

                History
                : 2 September 2016
                : 25 October 2016
                Funding
                Funded by: Galician Network of Drugs R+D REGID
                Award ID: R2014/025
                Funded by: General Directorate of Culture, Education and University Management of Xunta de Galicia
                Award ID: GRC2014/049
                Funded by: Collaborative Project on Medical Informatics (CIMED)
                Award ID: PI13/00280
                Funded by: Carlos III Health Institute from the Spanish National plan for Scientific
                Funded by: Technical Research and Innovation 2013–2016
                Funded by: European Regional Development Funds (FEDER)
                Funded by: Juan de la Cierva fellowship programme by the Spanish Ministry of Education
                Award ID: FJCI-2015-26071
                This study was supported by the Galician Network of Drugs R+D REGID (Xunta de Galicia R2014/025), by the General Directorate of Culture, Education and University Management of Xunta de Galicia (Ref. GRC2014/049) and by “Collaborative Project on Medical Informatics (CIMED)” PI13/00280 funded by the Carlos III Health Institute from the Spanish National plan for Scientific and Technical Research and Innovation 2013–2016 and the European Regional Development Funds (FEDER) and the Juan de la Cierva fellowship programme by the Spanish Ministry of Education (Carlos Fernandez-Lozano, Ref. FJCI-2015-26071). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Bioinformatics
                Computational Biology
                Molecular Biology
                Statistics
                Computational Science

                computational intelligence,machine learning,methodology,statistical analysis,experimental design,rrregrs

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