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An optimized feature selection technique based on bivariate copulas “GBCFS”

Author

Listed:
  • Karima Femmam

    (Mohamed Khider University of Biskra)

  • Brahim Brahimi

    (Mohamed Khider University of Biskra)

  • Smain Femmam

    (2 Rue des Fréres Lumiére)

Abstract

Recent years have shown that data pre-processing is an essential task in machine learning modeling, and one of the crucial steps in data pre-processing is dimensionality reduction, considering that many data used in machine learning are large datasets that contain redundant information, making them challenging to manage without reduction and cleaning. In this study, we offer an optimized method to reduce dimensions that combines bivariate copulas to feature selection. We use the copula function as a tool to detect inter-correlation and model the dependency (redundancy). Our method will be performed alongside well-known methods, and compared against them in term of reduction and classification accuracy using several models.

Suggested Citation

  • Karima Femmam & Brahim Brahimi & Smain Femmam, 2023. "An optimized feature selection technique based on bivariate copulas “GBCFS”," Journal of Combinatorial Optimization, Springer, vol. 45(2), pages 1-14, March.
    • Handle: RePEc:spr:jcomop:v:45:y:2023:i:2:d:10.1007_s10878-023-01006-9
    DOI: 10.1007/s10878-023-01006-9
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    References listed on IDEAS

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    1. Abrevaya, Jason, 1999. "Computation of the maximum rank correlation estimator," Economics Letters, Elsevier, vol. 62(3), pages 279-285, March.
    2. Fazel Badakhshan Farahabadi & Kianoush Fathi Vajargah & Rahman Farnoosh, 2021. "Dimension Reduction Big Data Using Recognition of Data Features Based on Copula Function and Principal Component Analysis," Advances in Mathematical Physics, Hindawi, vol. 2021, pages 1-8, July.
    3. Genest, Christian & Rémillard, Bruno & Beaudoin, David, 2009. "Goodness-of-fit tests for copulas: A review and a power study," Insurance: Mathematics and Economics, Elsevier, vol. 44(2), pages 199-213, April.
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