Detecting Bidding Fraud using a Few Labeled Data

Sulaf Elshaar, Samira Sadaoui

Abstract

Shill Bidding (SB) is a serious auction fraud committed by clever scammers. The challenge in labeling multi-dimensional SB training data hinders research on SB classification. To safeguard individuals from shill bidders, in this study, we explore Semi-Supervised Classification (SSC), which is the most suitable method for our fraud detection problem since SSC can learn efficiently from a few labeled data. To label a portion of SB data, we propose an anomaly detection method that we combine with hierarchical clustering. We carry out several experiments to determine statistically the minimal sufficient amount of labeled data required to achieve the highest accuracy. We also investigate the misclassified bidders to see where the misclassification occurs. The empirical analysis demonstrates that SSC reduces the laborious effort of labeling SB data.

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Paper Citation


in Harvard Style

Elshaar S. and Sadaoui S. (2020). Detecting Bidding Fraud using a Few Labeled Data.In Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-395-7, pages 17-25. DOI: 10.5220/0008894100170025


in Bibtex Style

@conference{icaart20,
author={Sulaf Elshaar and Samira Sadaoui},
title={Detecting Bidding Fraud using a Few Labeled Data},
booktitle={Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2020},
pages={17-25},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008894100170025},
isbn={978-989-758-395-7},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Detecting Bidding Fraud using a Few Labeled Data
SN - 978-989-758-395-7
AU - Elshaar S.
AU - Sadaoui S.
PY - 2020
SP - 17
EP - 25
DO - 10.5220/0008894100170025