@inproceedings{9a63ff871a434bf7be4efa9fd38d3af8,
title = "BAMTGAN: A Balanced Augmentation Technique for Tabular Data",
abstract = "This paper presents BAMTGAN, a novel data augmentation technique that addresses the class imbalance problem and prevents mode collapse by utilizing a modified DCGAN model and a new similarity loss to generate diverse and realistic tabular data. BAMTGAN encodes each column to produce a feature map for each record, which is then converted back to its original tabular form an intermediate image format. Experimental results demonstrate that BAMTGAN provides a more substantial improvement in developing high-quality predictive models than existing augmentation methods.",
keywords = "Data Augmentation, Deep Learning, GAN, Tabular data, Training data",
author = "Jueun Jeong and Hanseok Jeong and Kim, {Han Joon}",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 9th International Conference on Applied System Innovation, ICASI 2023 ; Conference date: 21-04-2023 Through 25-04-2023",
year = "2023",
doi = "10.1109/ICASI57738.2023.10179533",
language = "English",
series = "2023 9th International Conference on Applied System Innovation, ICASI 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "205--207",
editor = "Shoou-Jinn Chang and Sheng-Joue Young and Lam, {Artde Donald Kin-Tak} and Liang-Wen Ji and Prior, {Stephen D.}",
booktitle = "2023 9th International Conference on Applied System Innovation, ICASI 2023",
address = "United States",
}