Hybridizing graph‐based Gaussian mixture model with machine learning for classification of fraudulent transactions
Author:
Affiliation:
1. Department of Computer Science and Engineering National Institute of Technology Rourkela Rourkela India
2. Department of Computer Science and Engineering Birla Institute of Technology, Mesra, Ranchi Ranchi India
Publisher
Wiley
Subject
Artificial Intelligence,Computational Mathematics
Link
https://onlinelibrary.wiley.com/doi/pdf/10.1111/coin.12561
Reference52 articles.
1. PhuaC LeeV SmithK GaylerR.A comprehensive survey of data mining‐based fraud detection research. ArXiv preprint arXiv:1009.6119;2010.
2. ZeagerMF SridharA FogalN AdamsS BrownDE BelingPA.Adversarial learning in credit card fraud detection. Paper presented at: 2017 Systems and Information Engineering Design Symposium (SIEDS) IEEE;2017; pp.112–116.
3. Customs fraud detection
4. Binary ranking for ordinal class imbalance
5. PrustiD RathSK.Fraudulent transaction detection in credit card by applying ensemble machine learning techniques. Paper presented at: 2019 10th International Conference on Computing Communication and Networking Technologies (ICCCNT) IEEE;2019; pp.1–6.
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