Enhanced autoencoder-based fraud detection: a novel approach with noise factor encoding and SMOTE
Author:
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Hardware and Architecture,Human-Computer Interaction,Information Systems,Software
Link
https://link.springer.com/content/pdf/10.1007/s10115-023-02016-z.pdf
Reference30 articles.
1. Abdallah A, Maarof MA, Zainal A (2016) Fraud detection system: a survey. J Netw Comput Appl 68:90–113
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3. Yi H, Jiang Q, Yan X, Wang B (2020) Imbalanced classification based on minority clustering synthetic minority oversampling technique with wind turbine fault detection application. IEEE Trans Ind Inf 17(9):5867–5875
4. Wang W, Huang Y, Wang Y, Wang L (2014) Generalized autoencoder: a neural network framework for dimensionality reduction. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 490–497
5. Chicco D, Jurman G (2020) The advantages of the Matthews correlation coefficient (mcc) over f1 score and accuracy in binary classification evaluation. BMC Genom 21:1–13
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