{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T20:52:13Z","timestamp":1781124733421,"version":"3.54.1"},"reference-count":32,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2023,4,6]],"date-time":"2023-04-06T00:00:00Z","timestamp":1680739200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R&amp;D Program of China","award":["2019YFB1405802"],"award-info":[{"award-number":["2019YFB1405802"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>This paper proposes a method called autoencoder with probabilistic LightGBM (AED-LGB) for detecting credit card frauds. This deep learning-based AED-LGB algorithm first extracts low-dimensional feature data from high-dimensional bank credit card feature data using the characteristics of an autoencoder which has a symmetrical network structure, enhancing the ability of feature representation learning. The credit card fraud dataset comes from a real dataset anonymized by a bank and is highly imbalanced, with normal data far greater than fraud data. For this situation, the smote algorithm is used to resample the data before putting the extracted feature data into LightGBM, making the amount of fraud data and non-fraud data equal. After comparing the resampled and non-resampled data, it was found that the performance of the AED-LGB algorithm was not improved after resampling, and it was concluded that the AED-LGB algorithm is more suitable for imbalanced data. Finally, the AED-LGB algorithm is comparable with other commonly used machine learning algorithms, such as KNN and LightGBM, and it has an overall improvement of 2% in terms of the ACC index compared to LightGBM and KNN. When the threshold is set to 0.2, the MCC index of AED-LGB is 4% higher than that of the second-highest LightGBM algorithm and 30% higher than that of KNN. It shows that the AED-LGB algorithm has higher performance in accuracy, true positive rate, true negative rate, and Matthew\u2019s correlation coefficient.<\/jats:p>","DOI":"10.3390\/sym15040870","type":"journal-article","created":{"date-parts":[[2023,4,6]],"date-time":"2023-04-06T02:29:58Z","timestamp":1680748198000},"page":"870","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":47,"title":["AutoEncoder and LightGBM for Credit Card Fraud Detection Problems"],"prefix":"10.3390","volume":"15","author":[{"given":"Haichao","family":"Du","sequence":"first","affiliation":[{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"Shenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang 110168, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li","family":"Lv","sequence":"additional","affiliation":[{"name":"Shenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang 110168, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"An","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Computer & Information Engineering, Anyang Normal University, Anyang 455000, China"},{"name":"Key Laboratory of Oracle Bone Inscriptions Information Processing, Ministry of Education of China, Anyang 455000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongliang","family":"Wang","sequence":"additional","affiliation":[{"name":"Shenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang 110168, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,6]]},"reference":[{"key":"ref_1","unstructured":"de Best, R. (2021, October 10). Credit Card and Debit Card Number in the U.S. 2012\u20132018. Statista. Available online: https:\/\/www.statista.com\/statistics\/245385\/number-of-credit-cards-by-credit-card-type-in-the-united-states\/#statisticContainer."},{"key":"ref_2","unstructured":"Li, M.S., Yang, D., and Qin, Y.H. (2018, May 31). Anti-Fraud White Paper of Digital Finance. Available online: https:\/\/www.arx.cfa\/~\/media\/45620250D60C4DEFB081322259723D92.ashx."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"90068","DOI":"10.1109\/ACCESS.2022.3201099","article-title":"CGBNet: A Deep Learning Framework for Compost Classification","volume":"10","author":"Gangopadhyay","year":"2022","journal-title":"IEEE Access"},{"key":"ref_4","first-page":"25579","article-title":"A Multi-Level-Denoising Autoencoder Approach for Wind Turbine Fault Detection","volume":"8","author":"Wu","year":"2019","journal-title":"IEEE Access"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"25579","DOI":"10.1109\/ACCESS.2020.2971354","article-title":"An intelligent approach to credit card fraud detection using an optimized light gradient boosting machine","volume":"8","author":"Taha","year":"2020","journal-title":"IEEE Access"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"73103","DOI":"10.1109\/ACCESS.2021.3079701","article-title":"Predicting default risk on peer-to-peer lending imbalanced datasets","volume":"9","author":"Chen","year":"2021","journal-title":"IEEE Access"},{"key":"ref_7","unstructured":"Dal Pozzolo, A. (2015). Adaptive Machine Learning for Credit Card Fraud Detection. [Ph.D. Thesis, Universit\u00e9 Libre de Bruxelles]."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Lucas, Y., Portier, P.-E., Laporte, L., Calabretto, S., Caelen, O., He-Guelton, L., and Granitzer, M. (2019, January 8\u201312). Multiple perspectives HMM-based feature engineering for credit card fraud detection. Proceedings of the 34th ACM\/SIGAPP Symposium on Applied Computing, Limassol, Cyprus.","DOI":"10.1145\/3297280.3297586"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Awoyemi, J.O., Adetunmbi, A.O., and Oluwadare, S.A. (2017, January 29\u201331). Credit card fraud detection using machine learning techniques: A comparative analysis. Proceedings of the 2017 International Conference on Computing Networking and Informatics (ICCNI), Lagos, Nigeria.","DOI":"10.1109\/ICCNI.2017.8123782"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Zhang, F., Liu, G., Li, Z., Yan, C., and Jiang, C. (2019, January 14\u201319). GMM-based undersampling and its application for credit card fraud detection. Proceedings of the 2019 International Joint Conference on Neural Networks (IJCNN), Budapest, Hungary.","DOI":"10.1109\/IJCNN.2019.8852415"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Ahammad, J., Hossain, N., and Alam, M.S. (2020, January 10\u201312). Credit card fraud detection using data pre-processing on imbalanced data-Both oversampling and undersampling. Proceedings of the International Conference on Computing Advancements, Dhaka, Bangladesh.","DOI":"10.1145\/3377049.3377113"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1460","DOI":"10.1109\/TKDE.2012.99","article-title":"Anomaly detection via online oversampling principal component analysis","volume":"25","author":"Lee","year":"2012","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Wiese, B., and Omlin, C. (2009). Credit Card Transactions, Fraud Detection, and Machine Learning: Modelling Time with LSTM Recurrent Neural networks, Springer.","DOI":"10.1007\/978-3-642-04003-0_10"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1016\/j.eswa.2018.01.037","article-title":"Sequence classification for credit-card fraud detection","volume":"100","author":"Jurgovsky","year":"2018","journal-title":"Expert Syst. Appl."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"14277","DOI":"10.1109\/ACCESS.2018.2806420","article-title":"Credit card fraud detection using AdaBoost and majority voting","volume":"6","author":"Randhawa","year":"2018","journal-title":"IEEE Access"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"86101","DOI":"10.1109\/ACCESS.2022.3199425","article-title":"Feature engineering and resampling strategies for fund transfer fraud with limited transaction data and a time-inhomogeneous modi operandi","volume":"10","author":"Hsin","year":"2022","journal-title":"IEEE Access"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Naveen, P., and Diwan, B. (2020, January 7\u20139). Relative Analysis of ML Algorithm QDA, LR and SVM for Credit Card Fraud Detection Dataset. Proceedings of the 2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), Palladam, India.","DOI":"10.1109\/I-SMAC49090.2020.9243602"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Shirodkar, N., Mandrekar, P., Mandrekar, R.S., Sakhalkar, R., Kumar, K.C., and Aswale, S. (2020, January 24\u201325). Credit card fraud detection techniques\u2013A survey. Proceedings of the 2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE), Vellore, India.","DOI":"10.1109\/ic-ETITE47903.2020.112"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Malini, N., and Pushpa, M. (2017, January 27\u201328). Analysis on credit card fraud identification techniques based on KNN and outlier detection. Proceedings of the 2017 Third International Conference on Advances in Electrical, Electronics, Information, Communication and bio-Informatics (AEEICB), Chennai, India.","DOI":"10.1109\/AEEICB.2017.7972424"},{"key":"ref_20","first-page":"18","article-title":"Credit card fraud detection using deep learning based on auto-encoder and restricted boltzmann machine","volume":"9","author":"Pumsirirat","year":"2018","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Zamini, M., and Montazer, G. (2018, January 17\u201319). Credit card fraud detection using autoencoder based clustering. Proceedings of the 2018 9th International Symposium on Telecommunications (IST), Tehran, Iran.","DOI":"10.1109\/ISTEL.2018.8661129"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Krishna, M.V., and Praveenchandar, J. (2022, January 13\u201315). Comparative Analysis of Credit Card Fraud Detection using Logistic regression with Random Forest towards an Increase in Accuracy of Prediction. Proceedings of the 2022 International Conference on Edge Computing and Applications (ICECAA), Tamilnadu, India.","DOI":"10.1109\/ICECAA55415.2022.9936488"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Wulsin, D., Blanco, J., Mani, R., and Litt, B. (2010, January 12\u201314). Semi-supervised anomaly detection for EEG waveforms using deep belief nets. Proceedings of the 2010 Ninth International Conference on Machine Learning and Applications, Washington, DC, USA.","DOI":"10.1109\/ICMLA.2010.71"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Zhou, C., and Paffenroth, R.C. (2017, January 13\u201317). Anomaly detection with robust deep autoencoders. Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, NS, Canada.","DOI":"10.1145\/3097983.3098052"},{"key":"ref_25","unstructured":"Chalapathy, R., Menon, A.K., and Chawla, S. (2018). Anomaly detection using one-class neural networks. arXiv."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1388","DOI":"10.1109\/TKDE.2009.187","article-title":"Combating the small sample class imbalance problem using feature selection","volume":"22","author":"Wasikowski","year":"2009","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1467","DOI":"10.1109\/LGRS.2019.2900733","article-title":"Nonlinear unmixing of hyperspectral data via deep autoencoder networks","volume":"16","author":"Wang","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Liang, W., Luo, S., Zhao, G., and Wu, H. (2020). Predicting hard rock pillar stability using GBDT, XGBoost, and LightGBM algorithms. Mathematics, 8.","DOI":"10.3390\/math8050765"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"3034","DOI":"10.1109\/ACCESS.2022.3232287","article-title":"Fraud Detection in Banking Data by Machine Learning Techniques","volume":"11","author":"Hashemi","year":"2022","journal-title":"IEEE Access"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"116387","DOI":"10.1016\/j.eswa.2021.116387","article-title":"Geometric SMOTE for regression","volume":"2022","author":"Camacho","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_31","unstructured":"Misra, D. (2019). Mish: A self regularized non-monotonic activation function. arXiv."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.patrec.2020.03.030","article-title":"On the performance of Matthews correlation coefficient (MCC) for imbalanced dataset","volume":"136","author":"Zhu","year":"2020","journal-title":"Pattern Recognit. Lett."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/15\/4\/870\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:10:51Z","timestamp":1760123451000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/15\/4\/870"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,6]]},"references-count":32,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2023,4]]}},"alternative-id":["sym15040870"],"URL":"https:\/\/doi.org\/10.3390\/sym15040870","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,6]]}}}