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Article

Improved Predictive Ability of KPLS Regression with Memetic Algorithms

1
Faculty of Mathematical Science, Complutense University of Madrid, 28040 Madrid, Spain
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Financial & Actuarial Economics & Statistics Department, Faculty of Commerce and Tourism, Complutense University of Madrid, 28003 Madrid, Spain
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Polytechnic Faculty, National University of Asunción, San Lorenzo 111421, Paraguay
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Faculty of Exact and Technological Sciences, National University of Concepción, Concepción 010123, Paraguay
*
Author to whom correspondence should be addressed.
Academic Editor: Javier Alcaraz
Mathematics 2021, 9(5), 506; https://doi.org/10.3390/math9050506
Received: 10 December 2020 / Revised: 31 January 2021 / Accepted: 22 February 2021 / Published: 1 March 2021
Kernel partial least squares regression (KPLS) is a non-linear method for predicting one or more dependent variables from a set of predictors, which transforms the original datasets into a feature space where it is possible to generate a linear model and extract orthogonal factors also called components. A difficulty in implementing KPLS regression is determining the number of components and the kernel function parameters that maximize its performance. In this work, a method is proposed to improve the predictive ability of the KPLS regression by means of memetic algorithms. A metaheuristic tuning procedure is carried out to select the number of components and the kernel function parameters that maximize the cumulative predictive squared correlation coefficient, an overall indicator of the predictive ability of KPLS. The proposed methodology led to estimate optimal parameters of the KPLS regression for the improvement of its predictive ability. View Full-Text
Keywords: partial least squares regression; kernel-based method; cross-validation method; memetic algorithms partial least squares regression; kernel-based method; cross-validation method; memetic algorithms
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MDPI and ACS Style

Mello-Román, J.D.; Hernández, A.; Mello-Román, J.C. Improved Predictive Ability of KPLS Regression with Memetic Algorithms. Mathematics 2021, 9, 506. https://doi.org/10.3390/math9050506

AMA Style

Mello-Román JD, Hernández A, Mello-Román JC. Improved Predictive Ability of KPLS Regression with Memetic Algorithms. Mathematics. 2021; 9(5):506. https://doi.org/10.3390/math9050506

Chicago/Turabian Style

Mello-Román, Jorge D., Adolfo Hernández, and Julio C. Mello-Román 2021. "Improved Predictive Ability of KPLS Regression with Memetic Algorithms" Mathematics 9, no. 5: 506. https://doi.org/10.3390/math9050506

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