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How Data Augmentation Affects Evolutionary Algorithms in Feature Selection: An Experimental Study

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Abstract

The rapid growth of machine learning has led to increased features used to represent data, often resulting in superfluous or irrelevant features that negatively impact model performance. Feature selection techniques address this issue by identifying the smallest subset of relevant features. This paper examines the integration of a novel data augmentation algorithm with evolutionary algorithms for feature selection across ten datasets from widely varying domains. We investigate the effectiveness of Genetic Algorithms, Particle Swarm Optimization, and Differential Evolution on augmented datasets by 10–50% and compare their performance with standard filter-based and wrapper methods. Our experiments demonstrate that data augmentation significantly boosts the efficacy of evolutionary algorithms, improving accuracy by up to 5% and reducing feature sets by an average of 40%. While Differential Evolution generally outperforms other algorithms, our findings reveal that the efficacy of combining data augmentation and feature selection varies across datasets. Optimal performance is typically observed from 30 to 50% augmentation, though excessive augmentation can occasionally lead to slight performance degradation, emphasizing the need for careful calibration. This research paves the way for future studies on the interplay between data augmentation and feature selection, including investigations into explainability and generalizability across different machine learning paradigms. By providing insight into this complex interplay, our study contributes to developing more robust and efficient algorithms across various domains.

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The data are available to the corresponding author upon request.

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Funding

The research leading to these results has received funding from Project "Ecosistema dell’innovazione - Rome Technopole" financed by EU in NextGenerationEU plan through MUR Decree n. 1051 23.06.2022 - CUP H33C22000420001. This study was partially funded by the EU in NextGenerationEU Missione 4 Componente 1 CUP B53D23019150006 (PRIN 2022 "LBDigital" project). This study was partially funded by the EU in NextGenerationEU Missione 4 Componente 1 CUP H53D23003680006MU (PRIN 2022 "SHAPE-AD" project).

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Authors and Affiliations

  1. Department of Electrical and Information Engineering “Maurizio Scarano”, University of Cassino and Southern Lazio, Via G. Di Biasio 43, 03043, Cassino, Italy

    Emanuele Nardone, Tiziana D’Alessandro, Claudio De Stefano & Francesco Fontanella

  2. European University of Technology EUt+, European Union, Cassino, Italy

    Emanuele Nardone, Tiziana D’Alessandro, Claudio De Stefano & Francesco Fontanella

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  1. Emanuele Nardone
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  2. Tiziana D’Alessandro
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  4. Francesco Fontanella
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All authors contributed equally.

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Correspondence to Francesco Fontanella.

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Appendix A: Additional Results

Appendix A: Additional Results

See Table 8.

Table 8 Data augmentation and feature selection results in average accuracy and standard deviation computed over 20 runs for every feature selection technique and data augmentation (DA) percentage
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Nardone, E., D’Alessandro, T., De Stefano, C. et al. How Data Augmentation Affects Evolutionary Algorithms in Feature Selection: An Experimental Study. SN COMPUT. SCI. 6, 536 (2025). https://doi.org/10.1007/s42979-025-04049-3

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