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

Karima Femmam (), Brahim Brahimi () and Smain Femmam ()
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Karima Femmam: Mohamed Khider University of Biskra
Brahim Brahimi: Mohamed Khider University of Biskra
Smain Femmam: 2 Rue des Fréres Lumiére

Journal of Combinatorial Optimization, 2023, vol. 45, issue 2, No 20, 14 pages

Abstract: 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.

Keywords: Bivariate copulas; Bivariate analysis; Big data; Feature selection; Correlation (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10878-023-01006-9

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