Incremental Feature Learning for Fraud Data Stream

Armin Sadreddin, Samira Sadaoui

2022

Abstract

Our research addresses the actual behavior of the credit-card fraud detection environment where financial transactions containing sensitive data must not be amassed in a considerable amount to train robust classifiers. We introduce an adaptive learning approach that adjusts frequently and efficiently to new transaction chunks; each chunk is discarded after each training step. Our approach combines transfer learning and incremental feature learning. The former improves the feature relevancy for subsequent chunks, and the latter increases performance during training by dynamically determining the optimal network architecture for each new chunk. We show the effectiveness and efficiency of our approach experimentally on an actual fraud dataset.

Paper Citation

in Harvard Style

Sadreddin A. and Sadaoui S. (2022). Incremental Feature Learning for Fraud Data Stream. In Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART, ISBN 978-989-758-547-0, pages 268-275. DOI: 10.5220/0010812700003116

in Bibtex Style

@conference{icaart22,
author={Armin Sadreddin and Samira Sadaoui},
title={Incremental Feature Learning for Fraud Data Stream},
booktitle={Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,},
year={2022},
pages={268-275},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010812700003116},
isbn={978-989-758-547-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,
TI - Incremental Feature Learning for Fraud Data Stream
SN - 978-989-758-547-0
AU - Sadreddin A.
AU - Sadaoui S.
PY - 2022
SP - 268
EP - 275
DO - 10.5220/0010812700003116