When Two are Better Than One: Synthesizing Heavily Unbalanced Data
Nowadays, data is king and if treated and used properly it promises to give organizations a competitive edge over rivals by enabling them to develop and design Intelligent Systems to improve their services. However, they need to fully comply with not only ethical but also regulatory obligations, whe...
Saved in:
| Main Authors: | Francisco Ferreira, Nuno Lourenco, Bruno Cabral, Joao Paulo Fernandes |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2021-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9606863/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
A systematic review of privacy-preserving techniques for synthetic tabular health data
by: Tobias Hyrup, et al.
Published: (2025-03-01) -
Enhanced Conditional GAN for High-Quality Synthetic Tabular Data Generation in Mobile-Based Cardiovascular Healthcare
by: Malak Alqulaity, et al.
Published: (2024-11-01) -
Sharing is CAIRing: Characterizing principles and assessing properties of universal privacy evaluation for synthetic tabular data
by: Tobias Hyrup, et al.
Published: (2024-12-01) -
Tabular transformer generative adversarial network for heterogeneous distribution in healthcare
by: Ha Ye Jin Kang, et al.
Published: (2025-03-01) -
Data Augmentation-Based Enhancement for Efficient Network Traffic Classification
by: Chang-Yui Shin, et al.
Published: (2025-01-01)