Published June 1, 2022
| Version v1
Publication
Creation of Synthetic Data with Conditional Generative Adversarial Networks
Description
The generation of synthetic data is becoming a fundamental
task in the daily life of any organization due to new protection data
laws that are emerging. Generative Adversarial Networks (GANs) and
its variants have attracted many researchers in their research work due to
its elegant theoretical basis and its great performance in the generation of
new data [19]. The goal of synthetic data generation is to create data that
will perform similarly to the original dataset for many analysis tasks, such
as classification. The problem of GANs is that in a classification problem,
GANs do not take class labels into account when generating new data,
they treat it as another attribute. This research work has focused on the
creation of new synthetic data from the "Default of Credit Card Clients"
dataset with a Conditional Generative Adversarial Network (CGAN).
CGANs are an extension of GANs where the class label is taken into
account when the new data is generated. The performance of our results
has been measured by comparing the results obtained with classification
algorithms, both in the original dataset and in the data generated.
Additional details
Identifiers
- URL
- https://idus.us.es/handle//11441/133924
- URN
- urn:oai:idus.us.es:11441/133924