Computer Science > Machine Learning
[Submitted on 30 Mar 2022]
Title:Generation and Simulation of Synthetic Datasets with Copulas
View PDFAbstract:This paper proposes a new method to generate synthetic data sets based on copula models. Our goal is to produce surrogate data resembling real data in terms of marginal and joint distributions. We present a complete and reliable algorithm for generating a synthetic data set comprising numeric or categorical variables. Applying our methodology to two datasets shows better performance compared to other methods such as SMOTE and autoencoders.
Submission history
From: Stephan Robert-Nicoud [view email][v1] Wed, 30 Mar 2022 13:22:44 UTC (552 KB)
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