Handling Imbalanced Data for Credit Card Fraud Detection
Author:
Affiliation:
1. United International University Plot-2, United City, Madani Avenue, Bada,Department of Computer Science and Engineering,Dhaka,Bangladesh,1212
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9689773/9689775/09689866.pdf?arnumber=9689866
Reference19 articles.
1. Calibrating Probability with Undersampling for Unbalanced Classification
2. A Comparative Study of Machine Learning Techniques for Credit Card Fraud Detection Based on Time Variance
3. Rectified linear units improve restricted boltzmann machines;nair;ICML,2010
4. Random decision forests
5. Bagging predictors
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2. Enhanced particle swarm optimization-based hyperparameter optimized stacked autoencoder for credit card fraud detection;International Journal of Data Science and Analytics;2024-03-21
3. Enhancing Credit Card Fraud Detection: An Ensemble Machine Learning Approach;Big Data and Cognitive Computing;2024-01-03
4. Hybrid Undersampling and Oversampling for Handling Imbalanced Credit Card Data;IEEE Access;2024
5. Amount and Location Based Credit Card Fraud Detection;2023 Fifth International Conference on Electrical, Computer and Communication Technologies (ICECCT);2023-02-22
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