Generation and Simulation of Synthetic Datasets with Copulas

Autor:	Houssou, Regis, Augustin, Mihai-Cezar, Rappos, Efstratios, Bonvin, Vivien, Robert-Nicoud, Stephan
Rok vydání:	2022
Předmět:	Computer Science - Machine Learning Computer Science - Artificial Intelligence
Druh dokumentu:	Working Paper
Popis:	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.
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2203.17250 Zobrazit plný text záznamu View this record from Arxiv