{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,17]],"date-time":"2025-11-17T02:30:54Z","timestamp":1763346654341,"version":"3.45.0"},"reference-count":38,"publisher":"Tech Science Press","issue":"3","license":[{"start":{"date-parts":[[2025,10,26]],"date-time":"2025-10-26T00:00:00Z","timestamp":1761436800000},"content-version":"vor","delay-in-days":298,"URL":"https:\/\/doi.org\/10.32604\/TSP-CROSSMARKPOLICY"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["CMC"],"published-print":{"date-parts":[[2025]]},"DOI":"10.32604\/cmc.2025.067241","type":"journal-article","created":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T09:00:15Z","timestamp":1758618015000},"page":"5159-5181","update-policy":"https:\/\/doi.org\/10.32604\/tsp-crossmarkpolicy","source":"Crossref","is-referenced-by-count":0,"title":["Credit Card Fraud Detection Method Based on RF-WGAN-TCN"],"prefix":"10.32604","volume":"85","author":[{"given":"Ao","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Hongzhen","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Ruxin","family":"Liu","sequence":"additional","affiliation":[]}],"member":"17807","published-online":{"date-parts":[[2025]]},"reference":[{"key":"ref1","first-page":"234","article-title":"Credit-card fraud detection system using neural networks","volume":"20","author":"Al Balawi","year":"2023","journal-title":"Int Arab J Inf Technol"},{"key":"ref2","doi-asserted-by":"crossref","first-page":"113866","DOI":"10.1016\/j.dss.2022.113866","article-title":"CATCHM: a novel network-based credit card fraud detection method using node representation learning","volume":"164","author":"Van Belle","year":"2023","journal-title":"Decis Support Syst"},{"key":"ref3","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1016\/j.omega.2018.07.001","article-title":"Credit card fraud detection strategies with consumer incentives","volume":"88","author":"Wang","year":"2019","journal-title":"Omega"},{"key":"ref4","doi-asserted-by":"crossref","first-page":"93010","DOI":"10.1109\/ACCESS.2019.2927266","article-title":"An experimental study with imbalanced classification approaches for credit card fraud detection","volume":"7","author":"Makki","year":"2019","journal-title":"IEEE Access"},{"key":"ref5","doi-asserted-by":"crossref","first-page":"3391","DOI":"10.1016\/j.procs.2021.09.112","article-title":"Dataset shift assessment measures in monitoring predictive models","volume":"192","author":"Becker","year":"2021","journal-title":"Procedia Comput Sci"},{"key":"ref6","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1007\/s13748-016-0094-0","article-title":"Learning from imbalanced data: open challenges and future directions","volume":"5","author":"Krawczyk","year":"2016","journal-title":"Prog Artifi Intell"},{"key":"ref7","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE: synthetic minority over-sampling technique","volume":"16","author":"Chawla","year":"2002","journal-title":"J Artifi Intell Res"},{"key":"ref8","first-page":"731","article-title":"Under-sampling approaches for improving prediction of the minority class in an imbalanced dataset","volume":"344","author":"Yen","year":"2006","journal-title":"Lecture Notes Control Inf Sci"},{"key":"ref9","first-page":"102596","article-title":"Enhanced credit card fraud detection based on SVM-recursive feature elimination and hyper-parameters optimization","volume":"55","author":"Rtayli","year":"2020","journal-title":"J Inf Secur Appl"},{"key":"ref10","doi-asserted-by":"crossref","first-page":"165286","DOI":"10.1109\/ACCESS.2021.3134330","article-title":"Performance evaluation of machine learning methods for credit card fraud detection using SMOTE and AdaBoost","volume":"9","author":"Ileberi","year":"2021","journal-title":"IEEE Access"},{"key":"ref11","doi-asserted-by":"crossref","first-page":"6618841","DOI":"10.1155\/2021\/6618841","article-title":"Research on credit card default prediction based on k-means SMOTE and BP neural network","volume":"2021","author":"Chen","year":"2021","journal-title":"Complexity"},{"key":"ref12","doi-asserted-by":"crossref","first-page":"14050","DOI":"10.1109\/ACCESS.2024.3357091","article-title":"Hybrid undersampling and oversampling for handling imbalanced credit card data","volume":"12","author":"Alamri","year":"2024","journal-title":"IEEE Access"},{"key":"ref13","doi-asserted-by":"crossref","first-page":"16400","DOI":"10.1109\/ACCESS.2022.3148298","article-title":"A neural network ensemble with feature engineering for improved credit card fraud detection","volume":"10","author":"Esenogho","year":"2022","journal-title":"IEEE Access"},{"key":"ref14","doi-asserted-by":"crossref","first-page":"7194728","DOI":"10.1155\/2021\/7194728","article-title":"Solving misclassification of the credit card imbalance problem using near miss","volume":"2021","author":"Mqadi","year":"2021","journal-title":"Math Probl Eng"},{"key":"ref15","series-title":"2021 Feb 2\u20139; Virtual","first-page":"10165","article-title":"Addressing class imbalance in federated learning","volume":"35","author":"Wang","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"ref16","doi-asserted-by":"crossref","first-page":"107348","DOI":"10.1109\/ACCESS.2023.3320072","article-title":"Generative adversarial networks-based novel approach for fraud detection for the european cardholders 2013 dataset","volume":"11","author":"Almarshad","year":"2023","journal-title":"IEEE Access"},{"key":"ref17","doi-asserted-by":"crossref","first-page":"1054","DOI":"10.3390\/math11041054","article-title":"Financial time series forecasting with the deep learning ensemble model","volume":"11","author":"He","year":"2023","journal-title":"Mathematics"},{"key":"ref18","doi-asserted-by":"crossref","first-page":"1731","DOI":"10.1108\/K-04-2020-0239","article-title":"Fraud detection in bank transaction with wrapper model and Harris water optimization-based deep recurrent neural network","volume":"50","author":"Kolli","year":"2021","journal-title":"Kybernetes"},{"key":"ref19","doi-asserted-by":"crossref","first-page":"5080472","DOI":"10.1155\/2021\/5080472","article-title":"Research on default prediction for credit card users based on XGBoost-LSTM model","volume":"2021","author":"Gao","year":"2021","journal-title":"Discrete Dyn Nat Soc"},{"key":"ref20","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40537-021-00541-8","article-title":"Enhanced credit card fraud detection based on attention mechanism and LSTM deep model","volume":"8","author":"Benchaji","year":"2021","journal-title":"J Big Data"},{"key":"ref21","doi-asserted-by":"crossref","first-page":"106883","DOI":"10.1016\/j.asoc.2020.106883","article-title":"Ensemble of deep sequential models for credit card fraud detection","volume":"99","author":"Forough","year":"2021","journal-title":"Appl Soft Comput"},{"key":"ref22","doi-asserted-by":"crossref","first-page":"e12795","DOI":"10.1111\/exsy.12795","article-title":"Sequential credit card fraud detection: a joint deep neural network and probabilistic graphical model approach","volume":"39","author":"Forough","year":"2022","journal-title":"Expert Syst"},{"unstructured":"Liu Y, Qin G, Huang X, Wang J, Long M. Timer-XL: long-context transformers for unified time series forecasting. arXiv:2410.04803. 2024. doi:10.48550\/arXiv.2410.04803.","key":"ref23"},{"key":"ref24","first-page":"1","article-title":"VTformer: a novel multiscale linear transformer forecaster with variate-temporal dependency for multivariate time series","volume":"11","author":"Dai","year":"2025 Jun","journal-title":"Compl Intell Syst"},{"key":"ref25","doi-asserted-by":"crossref","first-page":"996","DOI":"10.1016\/j.ins.2022.07.022","article-title":"Meta-ADD: a meta-learning based pre-trained model for concept drift active detection","volume":"608","author":"Yu","year":"2022 Aug 1","journal-title":"Inf Sci"},{"key":"ref26","doi-asserted-by":"crossref","first-page":"109113","DOI":"10.1016\/j.patcog.2022.109113","article-title":"Detecting group concept drift from multiple data streams","volume":"134","author":"Yu","year":"2023 Feb 1","journal-title":"Pattern Recognit"},{"key":"ref27","series-title":"Proceedings of the 39th AAAI Conference on Artificial Intelligence. Vol. 39; 2025 Feb 22\u201326","first-page":"19124","article-title":"Early concept drift detection via prediction uncertainty","author":"Lu"},{"key":"ref28","series-title":"Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","first-page":"2924","article-title":"Online drift detection with maximum concept discrepancy","author":"Wan"},{"key":"ref29","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach Learn"},{"author":"Goodfellow","journal-title":"Advances in Neural Information Processing Systems 27 (NIPS 2014)","first-page":"2672","key":"ref30"},{"key":"ref31","series-title":"Proceedings of the 34th International Conference on Machine Learning","first-page":"214","article-title":"Wasserstein generative adversarial networks","author":"Arjovsky","year":"2017"},{"key":"ref32","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1073\/pnas.36.1.48","article-title":"Equilibrium points in n-person games","volume":"36","author":"Nash","year":"1950","journal-title":"Proc Natl Acad Sci U S A."},{"key":"ref33","series-title":"Computer Vision\u2014ECCV 2016 Workshops. Lecture Notes in Computer Science, Vol. 9914; 2016 Oct 8\u201310 and 15\u201316","first-page":"47","article-title":"Temporal convolutional networks: a unified approach to action segmentation","author":"Lea","year":"2016"},{"key":"ref34","series-title":"2015 IEEE Symposium Series on Computational Intelligence (SSCI); 2015 Dec 7\u201310","first-page":"159","article-title":"Calibrating probability with undersampling for unbalanced classification","author":"Dal Pozzolo","year":"2015"},{"key":"ref35","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1186\/s40537-023-00724-5","article-title":"Evaluating classifier performance with highly imbalanced big data","volume":"10","author":"Hancock","year":"2023","journal-title":"J Big Data"},{"key":"ref36","doi-asserted-by":"crossref","first-page":"2683","DOI":"10.3390\/math9212683","article-title":"Credit card fraud detection with autoencoder and probabilistic random forest","volume":"9","author":"Lin","year":"2021","journal-title":"Mathematics"},{"key":"ref37","doi-asserted-by":"crossref","first-page":"756","DOI":"10.3390\/electronics11050756","article-title":"A novel text2IMG mechanism of credit card fraud detection: a deep learning approach","volume":"11","author":"Alharbi","year":"2022","journal-title":"Electronics"},{"key":"ref38","doi-asserted-by":"crossref","first-page":"870","DOI":"10.3390\/sym15040870","article-title":"AutoEncoder and LightGBM for credit card fraud detection problems","volume":"15","author":"Du","year":"2023","journal-title":"Symmetry"}],"container-title":["Computers, Materials & Continua"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/cdn.techscience.cn\/files\/cmc\/2025\/TSP_CMC-85-3\/TSP_CMC_67241\/TSP_CMC_67241.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,17]],"date-time":"2025-11-17T02:27:40Z","timestamp":1763346460000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.techscience.com\/cmc\/v85n3\/64142"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":38,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2025]]},"published-print":{"date-parts":[[2025]]}},"URL":"https:\/\/doi.org\/10.32604\/cmc.2025.067241","relation":{},"ISSN":["1546-2226"],"issn-type":[{"type":"electronic","value":"1546-2226"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"2025-04-28","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-08-15","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-10-23","order":2,"name":"published","label":"Published Online","group":{"name":"publication_history","label":"Publication History"}}]}}