Modern metaheuristic with multi-objective formulation for the variable selection problem
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2017
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Resumo
The development of efficient algorithms for variable selection
becomes important to deal with large and complex datasets. Most works in
quantitative chemical analysis have used Genetic Algorithms (GAs) as a
reference method to select variables. On the other hand, new advances in
metaheuristic techniques provide novel possibilities in this task Moreover,
the application of Multi-Objective Optimization (MOO) may significantly
contribute to efficiently construct an accurate model in the context of
multivariate calibration. MOO has showed itself as an efficiently and
successful tool to dealing with conflicting objective-functions. For instance,
the use of MOO may be considered as a good choice to treat the reducing of
prediction error and the number of selected variables in a calibration model.
In this paper, we present a modern metaheuristic implementation called
Multi-Objective Firefly Algorithm (MOFA) for variable selection in
multivariate calibration models. The goal is to propose an optimization to
reduce the prediction error of the property of interest in the analysed sample
as well as reducing the number of selected variables. However, the
outcomes are remarkably promising compared with the previous work.
Based on the results obtained, it is possible to demonstrate that our proposal
is a viable alternative in order to deal with such conflicting objectives.
Additionally, we compare MOFA with a traditional GA implementation
and show that MOFA is more efficient for the variable selection problem.
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Palavras-chave
Variable selection, Multivariate calibration, Firefly algorithm
Citação
PAULA, Lauro Cassio Martins de et al. Modern metaheuristic with multi-objective formulation for the variable selection problem. Journal of Computer Science, Dubai, v. 13, n. 11, p. 659-666, 2017. DOI: 10.3844/jcssp.2017.659.666. Disponível em: https://thescipub.com/abstract/10.3844/jcssp.2017.659.666. Acesso em: 14 jun. 2023.