KPLS optimization approach using genetic algorithms | Zendy

Jorge Daniel Mello-Román | Zendy; Adolfo Hernández | Zendy

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KPLS optimization approach using genetic algorithms

Author(s) -

Jorge Daniel Mello-Román,

Adolfo Hernández

Publication year - 2020

Publication title -

procedia computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.334

H-Index - 76

ISSN - 1877-0509

DOI - 10.1016/j.procs.2020.03.051

Subject(s) - computer science , kernel (algebra) , convergence (economics) , generalization , algorithm , task (project management) , genetic algorithm , function (biology) , value (mathematics) , multivariate statistics , mathematical optimization , artificial intelligence , machine learning , mathematics , mathematical analysis , management , combinatorics , evolutionary biology , economics , biology , economic growth

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 Q2cum 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.

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