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A Survey on GAN Techniques for Data Augmentation to Address the Imbalanced Data Issues in Credit Card Fraud Detection
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Outgoing claims: the papers this work cites, builds on, or argues with.
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CNN‐ and GAN‐based classification of malicious code families: A code visualization approach
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Using Genetic Algorithm to Improve Classification of Imbalanced Datasets for Credit Card Fraud Detection
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Creation of Synthetic Data with Conditional Generative Adversarial Networks
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FFD: A Federated Learning Based Method for Credit Card Fraud Detection
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GAN-Based Anomaly Detection In Imbalance Problems
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Generative adversarial fusion network for class imbalance credit scoring
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GAN-based imbalanced data intrusion detection system
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Synthetic data augmentation for surface defect detection and classification using deep learning
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DGCNN: deep convolutional generative adversarial network based convolutional neural network for diagnosis of COVID-19
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Effective detection of sophisticated online banking fraud on extremely imbalanced data
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Applications of Generative Adversarial Networks (GANs): An Updated Review
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Group signature with restrictive linkability: minimizing privacy exposure in ubiquitous environment
inferredcites →https://doi.org/10.1007/s13198-016-0551-yopenalex-snapshot
A survey of machine-learning and nature-inspired based credit card fraud detection techniques
inferredcites →https://doi.org/10.1016/j.comcom.2022.07.034openalex-snapshot
A dynamic ensemble algorithm for anomaly detection in IoT imbalanced data streams
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A novel melanoma prediction model for imbalanced data using optimized SqueezeNet by bald eagle search optimization
inferredcites →https://doi.org/10.1016/j.eswa.2014.02.026openalex-snapshot
Learned lessons in credit card fraud detection from a practitioner perspective
inferredcites →https://doi.org/10.1016/j.eswa.2015.12.030openalex-snapshot
Feature engineering strategies for credit card fraud detection
inferredcites →https://doi.org/10.1016/j.eswa.2016.12.035openalex-snapshot
Learning from class-imbalanced data: Review of methods and applications
inferredcites →https://doi.org/10.1016/j.eswa.2020.113696openalex-snapshot
Improving classification accuracy using data augmentation on small data sets
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Conditional Wasserstein GAN-based oversampling of tabular data for imbalanced learning
inferredcites →https://doi.org/10.1016/j.ins.2017.12.030openalex-snapshot
Using generative adversarial networks for improving classification effectiveness in credit card fraud detection
inferredcites →https://doi.org/10.1016/j.knosys.2018.07.035openalex-snapshot
Imbalance: Oversampling algorithms for imbalanced classification in R
inferredcites →https://doi.org/10.1016/j.neucom.2021.12.093openalex-snapshot
GAN-based anomaly detection: A review
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Imbalance-XGBoost: leveraging weighted and focal losses for binary label-imbalanced classification with XGBoost
inferredcites →https://doi.org/10.1016/j.tele.2013.06.002openalex-snapshot
NFC mobile credit card: The next frontier of mobile payment?
inferredcites →https://doi.org/10.1088/1742-6596/1827/1/012066openalex-snapshot
Challenges and Corresponding Solutions of Generative Adversarial Networks (GANs): A Survey Study
inferredcites →https://doi.org/10.1109/access.2019.2905015openalex-snapshot
Recent Progress on Generative Adversarial Networks (GANs): A Survey
inferredcites →https://doi.org/10.1109/access.2019.2927266openalex-snapshot
An Experimental Study With Imbalanced Classification Approaches for Credit Card Fraud Detection
inferredcites →https://doi.org/10.1109/access.2021.3126656openalex-snapshot
When Two are Better Than One: Synthesizing Heavily Unbalanced Data
inferredcites →https://doi.org/10.1109/ccaa.2018.8777628openalex-snapshot
Data Augmentation using Generative models for Credit Card Fraud Detection
inferredcites →https://doi.org/10.1109/confluence.2019.8776942openalex-snapshot
Real-time Credit Card Fraud Detection Using Machine Learning
inferredcites →https://doi.org/10.1109/cvpr.2018.00098openalex-snapshot
GAGAN: Geometry-Aware Generative Adversarial Networks
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DP-CGAN: Differentially Private Synthetic Data and Label Generation
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A Learning-based Data Augmentation for Network Anomaly Detection
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A Generative Model of People in Clothing
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Generative Adversarial Minority Oversampling
inferredcites →https://doi.org/10.1109/icdm.2019.00014openalex-snapshot
Supervised Class Distribution Learning for GANs-Based Imbalanced Classification
inferredcites →https://doi.org/10.1109/icip.2017.8296650openalex-snapshot
Face aging with conditional generative adversarial networks
inferredcites →https://doi.org/10.1109/ictai.2007.71openalex-snapshot
Mining Data with Rare Events: A Case Study
inferredcites →https://doi.org/10.1109/iemcon.2018.8614815openalex-snapshot
Credit Card Fraud Detection Using Sparse Autoencoder and Generative Adversarial Network
inferredcites →https://doi.org/10.1109/iiphdw.2018.8388338openalex-snapshot
Data augmentation for improving deep learning in image classification problem
inferredcites →https://doi.org/10.1109/ijcnn.2019.8852415openalex-snapshot
GMM-based Undersampling and Its Application for Credit Card Fraud Detection
inferredcites →https://doi.org/10.1109/isi.2012.6284315openalex-snapshot
Payment card fraud: Challenges and solutions
inferredcites →https://doi.org/10.1109/msp.2017.2765202openalex-snapshot
Generative Adversarial Networks: An Overview
inferredcites →https://doi.org/10.1109/tdsc.2007.70228openalex-snapshot
Credit Card Fraud Detection Using Hidden Markov Model
inferredcites →https://doi.org/10.1109/tifs.2021.3139771openalex-snapshot
ScoreGAN: A Fraud Review Detector Based on Regulated GAN With Data Augmentation
inferredcites →https://doi.org/10.1109/tkde.2021.3130191openalex-snapshot
A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications
inferredcites →https://doi.org/10.1109/tkde.2022.3220219openalex-snapshot
A Survey on Deep Semi-Supervised Learning
inferredcites →https://doi.org/10.1109/tpami.2018.2832629openalex-snapshot
Imbalanced Deep Learning by Minority Class Incremental Rectification
inferredcites →https://doi.org/10.1109/tsmcb.2012.2187280openalex-snapshot
Multiclass Imbalance Problems: Analysis and Potential Solutions
inferredcites →https://doi.org/10.1109/uemcon47517.2019.8993085openalex-snapshot
Data Augmentation with Generative Models for Improved Malware Detection: A Comparative Study
inferredcites →https://doi.org/10.1145/3383455.3422554openalex-snapshot
Generating synthetic data in finance
inferredcites →https://doi.org/10.1145/3446374openalex-snapshot
Generative Adversarial Networks (GANs): Challenges, Solutions, and Future Directions
inferredcites →https://doi.org/10.1186/s40537-018-0138-3openalex-snapshot
Big Data fraud detection using multiple medicare data sources
inferredcites →https://doi.org/10.1186/s40537-019-0192-5openalex-snapshot
Survey on deep learning with class imbalance
inferredcites →https://doi.org/10.1186/s40537-019-0197-0openalex-snapshot
A survey on Image Data Augmentation for Deep Learning
inferredcites →https://doi.org/10.14569/ijacsa.2020.0111113openalex-snapshot
An Efficient Domain-Adaptation Method using GAN for Fraud Detection
inferredcites →https://doi.org/10.14778/3231751.3231757openalex-snapshot
Data synthesis based on generative adversarial networks
inferredcites →https://doi.org/10.1504/ijcsyse.2020.10033140openalex-snapshot
A survey on effects of class imbalance in data pre-processing stage of classification problem
inferredcites →https://doi.org/10.1609/aaai.v33i01.33011286openalex-snapshot
One-Class Adversarial Nets for Fraud Detection
inferredcites →https://doi.org/10.1613/jair.953openalex-snapshot
SMOTE: Synthetic Minority Over-sampling Technique
inferredcites →https://doi.org/10.23919/jcc.2021.10.004openalex-snapshot
Evolution and effectiveness of loss functions in generative adversarial networks
inferredcites →https://doi.org/10.30534/ijatcse/2021/811032021openalex-snapshot
Credit Card Fraud Detection using Imbalance Resampling Method with Feature Selection
inferredcites →https://doi.org/10.3390/app9204396openalex-snapshot
Machine Learning and Deep Learning Methods for Intrusion Detection Systems: A Survey
inferredcites →https://doi.org/10.3390/electronics10101216openalex-snapshot
Review on Generative Adversarial Networks: Focusing on Computer Vision and Its Applications
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Survey on Videos Data Augmentation for Deep Learning Models
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The Opening Capability for Security against Privacy Infringements in the Smart Grid Environment
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Alleviating Class Imbalance in Actuarial Applications Using Generative Adversarial Networks
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CBGRU: A Detection Method of Smart Contract Vulnerability Based on a Hybrid Model
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Generating Multi-label Discrete Patient Records using Generative Adversarial Networks
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Generative Adversarial Networks in Computer Vision: A Survey and Taxonomy
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Data Preprocessing For Supervised Leaning
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A novel deep learning-based hybrid Harris hawks with sine cosine approach for credit card fraud detection
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Ethical and Corporate Social Responsibility
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GAN-Induced Bias in Data Augmentation
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Implementation of Artificial Intelligence in Fraud Detection and Prevention Through a Systematic Literature Review and Its Implications for the Financial Sector
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