KPLS optimization approach using genetic algorithms

被引：6

作者：

Daniel Mello-Roman, Jorge ^{[1
]}

Hernandez, Adolfo ^{[2
]}

机构：

[1] Univ Complutense Madrid, Fac Ciencias Matemat, Madrid 28040, Spain

[2] Univ Complutense Madrid, Fac Comercio & Turismo, Madrid 28003, Spain

来源：

11TH INTERNATIONAL CONFERENCE ON AMBIENT SYSTEMS, NETWORKS AND TECHNOLOGIES (ANT) / THE 3RD INTERNATIONAL CONFERENCE ON EMERGING DATA AND INDUSTRY 4.0 (EDI40) / AFFILIATED WORKSHOPS | 2020年 / 170卷

关键词：

Partial Least Squares Regression; Kernel-based Methods; Cross-validation Method; Genetic Algorithm; LEAST-SQUARES REGRESSION; FEATURE-SELECTION; PLS;

D O I：

10.1016/j.procs.2020.03.051

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Kernel partial least squares regression (KPLS) is a technique widely used in the construction of predictive models. However, the adjustment of both the parameter of the kernel function and the number of components supposes for the researcher an unavoidable additional task. This paper presents a procedure that optimizes the generalization capacity of KPLS multivariate models using genetic algorithms (GA), selects the values of the kernel function parameter and the number of components for which the value of the cross -validation coefficient Q(cum)(2) is maximum, adds preliminary tests to configure the GA and defines a convergence criterion in terms of dispersion in the estimates. GA has demonstrated a good performance in the task of optimizing KPLS with convergent solutions towards a global optimum. (C) 2020 The Authors. Published by Elsevier B.V.

引用

页码：1153 / 1160

页数：8

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