Examinando por Materia "Aumento de datos"

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    Nonparametric Generation of Synthetic Data Using Copulas
    (Universidad EAFIT, 2023) Restrepo Lopera, Juan Pablo; Laniado Rodas, Henry; Rivera Agudelo, Juan Carlos; This research was funded by the call 852-2019 of the Ministry of Science, Technology and Innovation of the Republic of Colombia (MinCiencias), which allowed the development of the project with code 1216-852-72082 called “Descriptive and predictive analysis of the cement and concrete production process”
    This article presents a novel nonparametric approach to generate synthetic data using copulas, which are functions that explain the dependency structure of the real data. The proposed method addresses several challenges faced by existing synthetic data generation techniques, such as the preservation of complex multivariate structures presented in real data. By using all the information from real data and verifying that the generated synthetic data follows the same behavior as the real data under homogeneity tests, our method is a significant improvement over existing techniques. Our method is easy to implement and interpret, making it a valuable tool for solving class imbalance problems in machine learning models, improving the generalization capabilities of deep learning models, and anonymizing information in finance and healthcare domains, among other applications.