Multi-objective evolutionary algorithm for variable selection in calibration problems: A case study for protein concentration prediction

Lucena, Daniel Vitor de; Lima, Telma Woerle de; Soares, Anderson S.; Delbem, Alexandre C. B.; Filho, Arlindo Rodrigues Galvão; Coelho, Clarimar José; Laureano, Gustavo Teodoro

doi:10.1109/cec.2013.6557683

Cited by 9 publications

(9 citation statements)

References 22 publications

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“…As matrizes X e Y são separadas em X cal e Y cal para obter a matriz de coeficientes β e X teste e Y teste são usados para testar a precisão do modelo de predição. Os coeficientes em β podem ser obtidos pelo modelo de regressão linear de acordo com a equação (7).…”

Section: Calibração Multivariada E Seleção De Variáveisunclassified

“…A literatura sobre o problema de seleção de variáveis no contexto da quimiometria (veja referencias [3]- [6]) indica que o algoritmo evolutivo possui duas limitações para o problema analisado: (i) seleciona um número de variáveis maior do que modelos clássicos como o algoritmo das projeções sucessivas (APS) e (ii) os modelos gerados possuem uma maior sensibilidade à presença de ruídos instrumentais. Recentemente, Lucena [7], com o objetivo de reduzir o problema (i), propôs uma formulação multi-objetivo utilizando o algoritmo NSGA-II que minimiza o erro de predição e o número de variáveis selecionadas de forma simultânea. Os resultados demonstraram que a formulação com dois objetivos conseguiu minimizar simultaneamente os objetivos analisados a níveis melhores do que os algoritmos tradicionais.…”

Section: Introductionunclassified

“…A formulação original do algoritmo NSGA-II utilizada por Lucena et. al [7] foi proposta por Deb et. al [8].…”

Section: Introductionunclassified

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Algoritmo Evolutivo Multi-objetivo de Tabelas para Seleção de Variáveis em Calibração Multivariada

Jorge¹

2016

Anais Do 11. Congresso Brasileiro De Inteligência Computacional

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Section: Calibração Multivariada E Seleção De Variáveisunclassified

Section: Introductionunclassified

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Algoritmo Evolutivo Multi-objetivo de Tabelas para Seleção de Variáveis em Calibração Multivariada

Jorge¹

2016

Anais Do 11. Congresso Brasileiro De Inteligência Computacional

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“…We have used for comparison the Non-dominated Sorting Genetic Algorithm (NSGA-II), in particular, the same implementation of Lucena et al (2013). The main difference between NSGA-II and a simple GA is how the selection operator is applied.…”

Section: Setup Of the Algorithmsmentioning

confidence: 99%

“…A solution x 1 ∈Ω is called Pareto-optimal if there does not exist another solution that dominates it. Among non-dominated solutions, it is applied a multi-objective decision maker method described by Lucena et al (2013) to choose the current best. Algorithm 2 shows a pseudocode for the proposed MOFA.…”

Section: Multi-objective Optimizationmentioning

confidence: 99%

Modern Metaheuristic with Multi-Objective Formulation for the Variable Selection Problem

Paula

Soares

et al. 2017

Journal of Computer Science

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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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Epistasis-Based Feature Selection Algorithm

Paula

2021

Methods in Molecular Biology

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Multi-objective evolutionary algorithm for variable selection in calibration problems: A case study for protein concentration prediction

Cited by 9 publications

References 22 publications

Algoritmo Evolutivo Multi-objetivo de Tabelas para Seleção de Variáveis em Calibração Multivariada

Algoritmo Evolutivo Multi-objetivo de Tabelas para Seleção de Variáveis em Calibração Multivariada

Modern Metaheuristic with Multi-Objective Formulation for the Variable Selection Problem

Epistasis-Based Feature Selection Algorithm

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