Improved Predictive Ability of KPLS Regression with Memetic Algorithms

Jorge Daniel Mello-Román, Adolfo Hernández and Julio César Mello-Román
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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

Mathematics, 2021, vol. 9, issue 5, 1-13

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.

Keywords: partial least squares regression; kernel-based method; cross-validation method; memetic algorithms (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
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