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Multiobjective Firefly Algorithm for Variable Selection in Multivariate Calibration

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Book cover Progress in Artificial Intelligence (EPIA 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9273))

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Abstract

Firefly Algorithm is a newly proposed method with potential application on several real world problems, such as variable selection problem. This paper presents a Multiobjective Firefly Algorithm (MOFA) for variable selection in multivariate calibration models. The main objective is to propose an optimization to reduce the error value prediction of the property of interest, as well as reducing the number of variables selected. Based on the results obtained, it is possible to demonstrate that our proposal may be a viable alternative in order to deal with conflicting objective-functions. Additionally, we compare MOFA with traditional algorithms for variable selection and show that it is a more relevant contribution for the variable selection problem.

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References

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Author information

Authors and Affiliations

  1. Institute of Informatics, Federal University of Goiás, Goiânia, Goiás, Brazil

    Lauro Cássio Martins de Paula & Anderson da Silva Soares

Authors
  1. Lauro Cássio Martins de Paula
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  2. Anderson da Silva Soares
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Corresponding author

Correspondence to Lauro Cássio Martins de Paula .

Editor information

Editors and Affiliations

  1. ISEC - Coimbra Institute of Engineering, Polytechnic Institute of Coimbra, Coimbra, Portugal

    Francisco Pereira

  2. CIUSC, Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal

    Penousal Machado

  3. CIUSC, Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal

    Ernesto Costa

  4. CIUSC, Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal

    Amílcar Cardoso

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© 2015 Springer International Publishing Switzerland

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de Paula, L.C.M., da Silva Soares, A. (2015). Multiobjective Firefly Algorithm for Variable Selection in Multivariate Calibration. In: Pereira, F., Machado, P., Costa, E., Cardoso, A. (eds) Progress in Artificial Intelligence. EPIA 2015. Lecture Notes in Computer Science(), vol 9273. Springer, Cham. https://doi.org/10.1007/978-3-319-23485-4_27

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  • DOI: https://doi.org/10.1007/978-3-319-23485-4_27

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23484-7

  • Online ISBN: 978-3-319-23485-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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