Date: 03 November 2021 | Speaker: Thomas Poudevigne-Durance
The creation of synthetic data is increasingly important in a range of applications for example to increase dataset volume or to anonymise sensitive datasets. However, missing data in datasets is common, and is an issue for data analysis as calculations cannot be performed with missing data. There is thus a need for data synthesis methods capable of using datasets with missing data.
To achieve this, we propose a novel Generative Adversarial Model, or GAN algorithm, that creates synthetic data from datasets with missing values …..
