{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T17:46:12Z","timestamp":1742924772006,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":25,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819784868"},{"type":"electronic","value":"9789819784875"}],"license":[{"start":{"date-parts":[[2024,11,4]],"date-time":"2024-11-04T00:00:00Z","timestamp":1730678400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,4]],"date-time":"2024-11-04T00:00:00Z","timestamp":1730678400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-981-97-8487-5_34","type":"book-chapter","created":{"date-parts":[[2024,11,3]],"date-time":"2024-11-03T07:02:57Z","timestamp":1730617377000},"page":"490-504","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["SACTGAN-EE Imbalanced Data Processing Method for\u00a0Credit Default Prediction"],"prefix":"10.1007","author":[{"given":"Shuxian","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guoqiang","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhida","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,11,4]]},"reference":[{"issue":"1","key":"34_CR1","first-page":"99","volume":"11","author":"J Aduda","year":"2021","unstructured":"Aduda, J., Obondy, S.: Credit risk management and efficiency of savings and credit cooperative societies: a review of literature. J. Appl. Financ. Bank. 11(1), 99\u2013120 (2021)","journal-title":"J. Appl. Financ. Bank."},{"doi-asserted-by":"crossref","unstructured":"Aguiar, G., Krawczyk, B., Cano, A.: A survey on learning from imbalanced data streams: taxonomy, challenges, empirical study, and reproducible experimental framework. In: Machine learning pp. 1\u201379 (2023)","key":"34_CR2","DOI":"10.1007\/s10994-023-06353-6"},{"unstructured":"Ba, H.: Improving detection of credit card fraudulent transactions using generative adversarial networks (2019). arXiv preprint arXiv:1907.03355","key":"34_CR3"},{"key":"34_CR4","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla, N.V., Bowyer, K.W., Hall, L.O.: Smote: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321\u2013357 (2002)","journal-title":"J. Artif. Intell. Res."},{"key":"34_CR5","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1016\/j.ins.2019.07.070","volume":"505","author":"D Elreedy","year":"2019","unstructured":"Elreedy, D., Atiya, A.F.: A comprehensive analysis of synthetic minority oversampling technique (smote) for handling class imbalance. Inf. Sci. 505, 32\u201364 (2019)","journal-title":"Inf. Sci."},{"key":"34_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.114582","volume":"174","author":"J Engelmann","year":"2021","unstructured":"Engelmann, J., Lessmann, S.: Conditional wasserstein gan-based oversampling of tabular data for imbalanced learning. Expert Syst. Appl. 174, 114582 (2021)","journal-title":"Expert Syst. Appl."},{"unstructured":"Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. Advances in Neural Information Processing Systems, vol. 27 (2014)","key":"34_CR7"},{"key":"34_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2022.105669","volume":"118","author":"O Habibi","year":"2023","unstructured":"Habibi, O., Chemmakha, M., Lazaar, M.: Imbalanced tabular data modelization using CTGAN and machine learning to improve IoT botnet attacks detection. Eng. Appl. Artif. Intell. 118, 105669 (2023)","journal-title":"Eng. Appl. Artif. Intell."},{"key":"34_CR9","doi-asserted-by":"publisher","first-page":"220","DOI":"10.1016\/j.eswa.2016.12.035","volume":"73","author":"G Haixiang","year":"2017","unstructured":"Haixiang, G., Yijing, L., Shang, J., Mingyun, G., Yuanyue, H., Bing, G.: Learning from class-imbalanced data: review of methods and applications. Expert Syst. Appl. 73, 220\u2013239 (2017)","journal-title":"Expert Syst. Appl."},{"key":"34_CR10","doi-asserted-by":"publisher","first-page":"3687","DOI":"10.1007\/s13042-019-00953-2","volume":"10","author":"X Han","year":"2019","unstructured":"Han, X., Cui, R., Lan, Y., Kang, Y., Deng, J.: A gaussian mixture model based combined resampling algorithm for classification of imbalanced credit data sets. Int. J. Mach. Learn. Cybern. 10, 3687\u20133699 (2019)","journal-title":"Int. J. Mach. Learn. Cybern."},{"unstructured":"He, H., Bai, Y., Garcia, E., Li, S.A.: Adaptive synthetic sampling approach for imbalanced learning. In: IEEE International Joint Conference on Neural Networks and IEEE World Congress On Computational Intelligence, vol.\u00a02008 (2008)","key":"34_CR11"},{"key":"34_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.114411","volume":"168","author":"JW Lee","year":"2021","unstructured":"Lee, J.W., Lee, W.K., Sohn, S.Y.: Graph convolutional network-based credit default prediction utilizing three types of virtual distances among borrowers. Expert Syst. Appl. 168, 114411 (2021)","journal-title":"Expert Syst. Appl."},{"doi-asserted-by":"crossref","unstructured":"Liu, X.Y., Wu, J., Zhou, Z.H.: Exploratory undersampling for class-imbalance learning. IEEE Trans. Syst. Man Cybern. Part B (Cybernetics) 39(2), 539\u2013550 (2008)","key":"34_CR13","DOI":"10.1109\/TSMCB.2008.2007853"},{"doi-asserted-by":"crossref","unstructured":"Loezer, L., Enembreck, F., Barddal, J.P., de\u00a0Souza Britto\u00a0Jr, A.: Cost-sensitive learning for imbalanced data streams. In: Proceedings of the 35th Annual ACM Symposium on Applied Computing, pp. 498\u2013504 (2020)","key":"34_CR14","DOI":"10.1145\/3341105.3373949"},{"doi-asserted-by":"crossref","unstructured":"Lusa, L., et\u00a0al.: Evaluation of smote for high-dimensional class-imbalanced microarray data. In: 2012 11th International Conference on Machine Learning and Applications, vol.\u00a02, pp. 89\u201394. IEEE (2012)","key":"34_CR15","DOI":"10.1109\/ICMLA.2012.183"},{"key":"34_CR16","doi-asserted-by":"publisher","first-page":"381","DOI":"10.1016\/j.eswa.2018.06.011","volume":"110","author":"S Nami","year":"2018","unstructured":"Nami, S., Shajari, M.: Cost-sensitive payment card fraud detection based on dynamic random forest and k-nearest neighbors. Expert Syst. Appl. 110, 381\u2013392 (2018)","journal-title":"Expert Syst. Appl."},{"issue":"1","key":"34_CR17","doi-asserted-by":"publisher","first-page":"925","DOI":"10.2991\/ijcis.11.1.70","volume":"11","author":"A Namvar","year":"2018","unstructured":"Namvar, A., Siami, M., Rabhi, F., Naderpour, M.: Credit risk prediction in an imbalanced social lending environment. Int. J. Comput. Intell. Syst. 11(1), 925\u2013935 (2018)","journal-title":"Int. J. Comput. Intell. Syst."},{"key":"34_CR18","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1016\/j.ins.2020.05.040","volume":"536","author":"K Niu","year":"2020","unstructured":"Niu, K., Zhang, Z., Liu, Y., Li, R.: Resampling ensemble model based on data distribution for imbalanced credit risk evaluation in p2p lending. Inf. Sci. 536, 120\u2013134 (2020)","journal-title":"Inf. Sci."},{"key":"34_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.techfore.2020.120462","volume":"165","author":"S Pang","year":"2021","unstructured":"Pang, S., Hou, X., Xia, L.: Borrowers\u2019 credit quality scoring model and applications, with default discriminant analysis based on the extreme learning machine. Technol. Forecast. Soc. Chang. 165, 120462 (2021)","journal-title":"Technol. Forecast. Soc. Chang."},{"key":"34_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2020.106759","volume":"97","author":"M Saini","year":"2020","unstructured":"Saini, M., Susan, S.: Deep transfer with minority data augmentation for imbalanced breast cancer dataset. Appl. Soft Comput. 97, 106759 (2020)","journal-title":"Appl. Soft Comput."},{"key":"34_CR21","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1016\/j.ins.2017.10.017","volume":"425","author":"J Sun","year":"2018","unstructured":"Sun, J., Lang, J., Fujita, H., Li, H.: Imbalanced enterprise credit evaluation with DTE-SBD: decision tree ensemble based on smote and bagging with differentiated sampling rates. Inf. Sci. 425, 76\u201391 (2018)","journal-title":"Inf. Sci."},{"unstructured":"Xu, L., Skoularidou, M., Veeramachaneni, K.: Modeling tabular data using conditional gan. Advances in Neural Information Processing Systems, vol. 32 (2019)","key":"34_CR22"},{"unstructured":"Xu, L., Veeramachaneni, K.: Synthesizing tabular data using generative adversarial networks (2018). arXiv preprint arXiv:1811.11264","key":"34_CR23"},{"key":"34_CR24","doi-asserted-by":"publisher","first-page":"192","DOI":"10.1016\/j.asoc.2018.04.049","volume":"69","author":"L Yu","year":"2018","unstructured":"Yu, L., Zhou, R., Tang, L., Chen, R.: A DBN-based resampling SVM ensemble learning paradigm for credit classification with imbalanced data. Appl. Soft Comput. 69, 192\u2013202 (2018)","journal-title":"Appl. Soft Comput."},{"issue":"1","key":"34_CR25","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1109\/TNNLS.2018.2832648","volume":"30","author":"C Zhang","year":"2018","unstructured":"Zhang, C., Tan, K.C., Li, H., Hong, G.S.: A cost-sensitive deep belief network for imbalanced classification. IEEE Trans. Neural Netw. Learn. Syst. 30(1), 109\u2013122 (2018)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition and Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-97-8487-5_34","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,3]],"date-time":"2024-11-03T07:10:33Z","timestamp":1730617833000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-97-8487-5_34"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,4]]},"ISBN":["9789819784868","9789819784875"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-981-97-8487-5_34","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,11,4]]},"assertion":[{"value":"4 November 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chinese Conference on Pattern Recognition and Computer Vision  (PRCV)","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Urumqi","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 October 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ccprcv2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/2024.prcv.cn\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}