Dual Autoencoders Generative Adversarial Network for Imbalanced Classification Problem

Wu, Ensen; Cui, Hongyan; Welsch, Roy E.

doi:10.1109/access.2020.2994327

Cited by 18 publications

(7 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Each of the two networks is typically a DNN with multiple layers interconnected. GAN appeared in seven articles ( Ba, 2019 ; Fiore et al, 2019 ; Tingfei, Guangquan & Kuihua, 2020 ; Hwang & Kim, 2020 ; Niu, Wang & Yang, 2019 ; Wu, Cui & Welsch, 2020 ; Veigas, Regulagadda & Kokatnoor, 2021 ). In Ba (2019) , GANs employed as an oversampling technique.…”

Section: Results and Analysismentioning

confidence: 99%

See 1 more Smart Citation

A systematic review of literature on credit card cyber fraud detection using machine and deep learning

Btoush

Zhou

Gururajan

et al. 2023

PeerJ Computer Science

View full text Add to dashboard Cite

The increasing spread of cyberattacks and crimes makes cyber security a top priority in the banking industry. Credit card cyber fraud is a major security risk worldwide. Conventional anomaly detection and rule-based techniques are two of the most common utilized approaches for detecting cyber fraud, however, they are the most time-consuming, resource-intensive, and inaccurate. Machine learning is one of the techniques gaining popularity and playing a significant role in this field. This study examines and synthesizes previous studies on the credit card cyber fraud detection. This review focuses specifically on exploring machine learning/deep learning approaches. In our review, we identified 181 research articles, published from 2019 to 2021. For the benefit of researchers, review of machine learning/deep learning techniques and their relevance in credit card cyber fraud detection is presented. Our review provides direction for choosing the most suitable techniques. This review also discusses the major problems, gaps, and limits in detecting cyber fraud in credit card and recommend research directions for the future. This comprehensive review enables researchers and banking industry to conduct innovation projects for cyber fraud detection.

show abstract

Section: Results and Analysismentioning

confidence: 99%

“…In Wu, Cui & Welsch (2020) , dual autoencoders generative adversarial networks (DAEGAN) is employed for the imbalanced classification problem. The new model trains GAN to duplicate fraudulent transaction for autoencoder training.…”

Section: Results and Analysismentioning

confidence: 99%

A systematic review of literature on credit card cyber fraud detection using machine and deep learning

Btoush

Zhou

Gururajan

et al. 2023

PeerJ Computer Science

View full text Add to dashboard Cite

show abstract

“…Finally, the model carried out the detection of credit card fraud in the training package generated under the WGAN model. The detailed phases for DAEGAN are shown in Figure 13 [20]. Researchers first used real transactions made by European cardholders where have 492 frauds out of 284,807 transactions and fraud data generated by WGAN as the data set.…”

Section: Deaganmentioning

confidence: 99%

Researches Advanced in Finance based on Generative Adversarial Networks

Chen¹,

Wang²,

Zhang³

2023

HSET

View full text Add to dashboard Cite

Topics about how to apply Generative Adversarial Networks (GANs) to finance industry and research to promote financial market prosperity and progress with deep learning technology never fail to arouse public attention. For instance, financial academic realm such as portfolio optimization, trade execution strategies and financial information progressing have made use of machine learning. After a large number of literature retrieval and attempts to classify from different aspects, we ultimately find out that GAN is one of the most famous and appropriate technologies widely adopted in academic research and commercial applications. Aiming at assisting reader with having insight into how GAN improved financial work’s efficiency, in this paper, we propose three research perspectives of GAN in financial work: Stock Market Prediction, Fault Detection, and Time Series. By organizing from an innovative and macro perspective, we present and classify the application methods of GAN and the improvement of the basic model of GAN in order to adapt to the corresponding fields under the corresponding research directions. In general, the following improved models based on the basic GAN model for the corresponding financial perspective are involved: GAN of three-layer dense network, GAN with the MLP and LSTM separately as the discriminator and the generator, GAN-FD stock projection model, BERT and GAN in Stock Prediction; SSGANs, DAEGAN in Fault Detection; SeqGAN, Quant GAN, RNN-GAN, PSA-GAN in Time Series.

show abstract

“…Lam and Hsiao [20] proposed a neural network-based method that uses the generation of adversarial networks to generate missing values, research shows that the generated 'fake' data can simulate real data and perform better on the test set. Wu et al [21] proposed a dual autoencoder to generate a adversarial network which shows a good classification capability in ablation study.…”

Section: Related Literaturementioning

confidence: 99%

Credit Scoring Model in Imbalanced Data Based on CNN-ATCN

Peng

Zhang

2021

Preprint

View full text Add to dashboard Cite

With the rapid growth of credit card and personal loan in finance industry, how to detect a potential default or bad debt with limited information has become extremely crucial. Meanwhile, one of the troublesome challenges in the field of credit scoring is the lack of positive samples. In this paper, we firstly introduced the idea of conditional tabular generative adversarial network(CTGAN) to generate sufficient default transactions into the origin data. Then we proposed a hybrid ensemble learning model based on CNN-ATCN to extract static features and dynamic features simultaneously, which CNN was utilized for finance attribute learning while the TCN with attention mechanism was used for extracting temporal dependencies from data. And LR, XGBoost, Adaboost, Random Forest are regarded as heterogeneous individual learners to form a stacking machine to output the classification results. We verified the designed default risk prediction model in two real world datasets. The results of the experiment indicate that CTGAN can effectively solve the data imbalance problem and the proposed CNN-ATCN model outperforms other state-of-art deep learning models in various way of metrics.

show abstract

Dual Autoencoders Generative Adversarial Network for Imbalanced Classification Problem

Cited by 18 publications

References 24 publications

A systematic review of literature on credit card cyber fraud detection using machine and deep learning

A systematic review of literature on credit card cyber fraud detection using machine and deep learning

Researches Advanced in Finance based on Generative Adversarial Networks

Credit Scoring Model in Imbalanced Data Based on CNN-ATCN

Contact Info

Product

Resources

About