An Approach for Improving Oversampling by Filtering out Unrealistic Synthetic Data

Nada Boudegzdame, Karima Sedki, Rosy Tspora, Rosy Tspora, Rosy Tspora, Jean-Baptiste Lamy

2024

Abstract

Oversampling algorithms are commonly used in machine learning to address class imbalance by generating new synthetic samples of the minority class. While oversampling can improve classification models’ performance on minority classes, our research reveals that models often learn to detect noise generated by oversampling algorithms rather than the underlying patterns. To overcome this issue, this article proposes a method that involves identifying and filtering unrealistic synthetic data, using advanced technique such a neural network for detecting unrealistic synthetic data samples. This aims to enhance the quality of the oversampled datasets and improve machine learning models’ ability to uncover genuine patterns. The effectiveness of the proposed approach is thoroughly examined and evaluated, demonstrating enhanced model performance.

Paper Citation

in Harvard Style

Boudegzdame N., Sedki K., Tspora R. and Lamy J. (2024). An Approach for Improving Oversampling by Filtering out Unrealistic Synthetic Data. In Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-680-4, SciTePress, pages 291-298. DOI: 10.5220/0012325400003636

in Bibtex Style

@conference{icaart24,
author={Nada Boudegzdame and Karima Sedki and Rosy Tspora and Jean-Baptiste Lamy},
title={An Approach for Improving Oversampling by Filtering out Unrealistic Synthetic Data},
booktitle={Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2024},
pages={291-298},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012325400003636},
isbn={978-989-758-680-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - An Approach for Improving Oversampling by Filtering out Unrealistic Synthetic Data
SN - 978-989-758-680-4
AU - Boudegzdame N.
AU - Sedki K.
AU - Tspora R.
AU - Lamy J.
PY - 2024
SP - 291
EP - 298
DO - 10.5220/0012325400003636
PB - SciTePress