Semi-supervised Classification of Fraud Data in Commercial Auctions
Author:
Affiliation:
1. Computer Science Department, University of Regina, Regina, Canada
Publisher
Informa UK Limited
Subject
Artificial Intelligence
Link
https://www.tandfonline.com/doi/pdf/10.1080/08839514.2019.1691341
Reference15 articles.
1. Anowar, F., S. Sadaoui, and M. Mouhoub 2018. “Auction Fraud Classification Based on Clustering and Sampling Techniques”. IEEE 17th International Conference on Machine Learning and Applications, ICMLA, Dec. Orlando, FL, USA. doi: doi:10.1109/ICMLA.2018.00061
2. SMOTE: Synthetic Minority Over-sampling Technique
3. Price comparison: A reliable approach to identifying shill bidding in online auctions?
4. Elshaar, S., and S. Sadaoui. 2019. Building High-quality Auction Fraud Dataset. Computer and Information Science; Vol. 12, No. 4; 2019, ISSN 1913-8989, E-ISSN 1913-8997, Canada. doi:10.5539/cis.v12n4p1
5. A Real-Time Self-Adaptive Classifier for Identifying Suspicious Bidders in Online Auctions
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