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Improved Predictive Ability of KPLS Regression with Memetic Algorithms

Author

Listed:
  • Jorge Daniel Mello-Román

    (Faculty of Mathematical Science, Complutense University of Madrid, 28040 Madrid, Spain)

  • Adolfo Hernández

    (Financial & Actuarial Economics & Statistics Department, Faculty of Commerce and Tourism, Complutense University of Madrid, 28003 Madrid, Spain)

  • Julio César Mello-Román

    (Polytechnic Faculty, National University of Asunción, San Lorenzo 111421, Paraguay
    Faculty of Exact and Technological Sciences, National University of Concepción, Concepción 010123, Paraguay)

Abstract

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.

Suggested Citation

  • Jorge Daniel Mello-Román & Adolfo Hernández & Julio César Mello-Román, 2021. "Improved Predictive Ability of KPLS Regression with Memetic Algorithms," Mathematics, MDPI, vol. 9(5), pages 1-13, March.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:5:p:506-:d:508261
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    References listed on IDEAS

    as
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    3. Pablo Moscato & Carlos Cotta, 2010. "A Modern Introduction to Memetic Algorithms," International Series in Operations Research & Management Science, in: Michel Gendreau & Jean-Yves Potvin (ed.), Handbook of Metaheuristics, chapter 0, pages 141-183, Springer.
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    5. Bergmeir, Christoph & Molina, Daniel & Benítez, José M., 2016. "Memetic Algorithms with Local Search Chains in R: The Rmalschains Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 75(i04).
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