Credit Card Fraud Detection Using Weighted Support Vector Machine

Applied Mathematics

ISSN Print: 2152-7385
ISSN Online: 2152-7393
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"Credit Card Fraud Detection Using Weighted Support Vector Machine"
written by Dongfang Zhang, Basu Bhandari, Dennis Black,
published by Applied Mathematics, Vol.11 No.12, 2020

has been cited by the following article(s):

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[9]	Credit Card Fraud Detection Method Based on KRSMOTE+ENN and XGBoost Algorithm Proceedings of the 5th International Conference on Computer Information and Big Data Applications, 2024 DOI:10.1145/3671151.3671365

[10]	K‐Fuse: Credit card fraud detection based on a classification method with a priori class partitioning and a novel feature selection strategy Applied Stochastic Models in Business and Industry, 2024 DOI:10.1002/asmb.2868

[11]	Advancing Model Performance With ADASYN and Recurrent Feature Elimination and Cross-Validation in Machine Learning-Assisted Credit Card Fraud Detection: A Comparative Analysis IEEE Access, 2024 DOI:10.1109/ACCESS.2024.3457922

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[17]	An optimal approach for fraud detection by comparing random forest algorithm and support vector machine algorithm for credit card transaction with improved accuracy CONTEMPORARY INNOVATIONS IN ENGINEERING AND MANAGEMENT, 2023 DOI:10.1063/5.0177045

[18]	Chinese corporate fraud risk assessment with machine learning Intelligent Systems with Applications, 2023 DOI:10.1016/j.iswa.2023.200294

[19]	Forging a User-Trust Memetic Modular Neural Network Card Fraud Detection Ensemble: A Pilot Study Journal of Computing Theories and Applications, 2023 DOI:10.33633/jcta.v1i2.9259

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[21]	Comprehensive Analysis of Resampling Methods on Ensemble Learning for Credit Card Fraud Detection Afyon Kocatepe University Journal of Sciences and Engineering, 2022 DOI:10.35414/akufemubid.1066453

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