{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T13:18:57Z","timestamp":1768828737632,"version":"3.49.0"},"publisher-location":"Cham","reference-count":20,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783031217524","type":"print"},{"value":"9783031217531","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-21753-1_51","type":"book-chapter","created":{"date-parts":[[2022,11,20]],"date-time":"2022-11-20T10:02:32Z","timestamp":1668938552000},"page":"527-539","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Solving Multi-class Imbalance Problems Using Improved Tabular GANs"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3996-2656","authenticated-orcid":false,"given":"Zakarya","family":"Farou","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liudmila","family":"Kopeikina","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9438-840X","authenticated-orcid":false,"given":"Tom\u00e1\u0161","family":"Horv\u00e1th","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,11,21]]},"reference":[{"issue":"1","key":"51_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10515-021-00311-z","volume":"29","author":"A Balaram","year":"2022","unstructured":"Balaram, A., Vasundra, S.: Prediction of software fault-prone classes using ensemble random forest with adaptive synthetic sampling algorithm. Autom. Softw. Eng. 29(1), 1\u201321 (2022)","journal-title":"Autom. Softw. Eng."},{"issue":"3","key":"51_CR2","doi-asserted-by":"publisher","first-page":"228","DOI":"10.1093\/jamia\/ocy142","volume":"26","author":"MK Baowaly","year":"2019","unstructured":"Baowaly, M.K., Lin, C.C., Liu, C.L., Chen, K.T.: Synthesizing electronic health records using improved generative adversarial networks. J. Am. Med. Inform. Assoc. 26(3), 228\u2013241 (2019)","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"51_CR3","first-page":"1","volume":"22","author":"G Biau","year":"2021","unstructured":"Biau, G., Sangnier, M., Tanielian, U.: Some theoretical insights into Wasserstein GANs. J. Mach. Learn. Res. 22, 1\u201345 (2021)","journal-title":"J. Mach. Learn. Res."},{"key":"51_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.116387","volume":"193","author":"L Camacho","year":"2022","unstructured":"Camacho, L., Douzas, G., Bacao, F.: Geometric SMOTE for regression. Expert Syst. Appl. 193, 116387 (2022)","journal-title":"Expert Syst. Appl."},{"issue":"3","key":"51_CR5","first-page":"35","volume":"7","author":"V Dogra","year":"2022","unstructured":"Dogra, V., Verma, S., Jhanjhi, N., Ghosh, U., Le, D.N., et al.: A comparative analysis of machine learning models for banking news extraction by multiclass classification with imbalanced datasets of financial news: challenges and solutions. Int. J. Interact. Multimedia Artif. Intell. 7(3), 35\u201353 (2022)","journal-title":"Int. J. Interact. Multimedia Artif. Intell."},{"key":"51_CR6","unstructured":"Dua, D., Graff, C.: UCI machine learning repository (2019). http:\/\/archive.ics.uci.edu\/ml"},{"key":"51_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1007\/978-3-030-62365-4_6","volume-title":"Intelligent Data Engineering and Automated Learning \u2013 IDEAL 2020","author":"Z Farou","year":"2020","unstructured":"Farou, Z., Mouhoub, N., Horv\u00e1th, T.: Data generation using gene expression generator. In: Analide, C., Novais, P., Camacho, D., Yin, H. (eds.) IDEAL 2020. LNCS, vol. 12490, pp. 54\u201365. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-62365-4_6"},{"key":"51_CR8","series-title":"Lecture Notes in Electrical Engineering","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1007\/978-981-16-8892-8_12","volume-title":"Recent Innovations in Computing","author":"Z Farou","year":"2022","unstructured":"Farou, Z., Ouaari, S., Domian, B., Horv\u00e1th, T.: Directed undersampling using active learning for particle identification. In: Singh, P.K., Singh, Y., Chhabra, J.K., Ill\u00e9s, Z., Verma, C. (eds.) Recent Innovations in Computing. Lecture Notes in Electrical Engineering, vol. 855, pp. 149\u2013162. Springer, Singapore (2022). https:\/\/doi.org\/10.1007\/978-981-16-8892-8_12"},{"key":"51_CR9","doi-asserted-by":"crossref","unstructured":"Feng, Q., Guo, C., Benitez-Quiroz, F., Martinez, A.M.: When do GANs replicate? On the choice of dataset size. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 6701\u20136710, October 2021","DOI":"10.1109\/ICCV48922.2021.00663"},{"key":"51_CR10","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-98074-4","volume-title":"Learning from Imbalanced Data Sets","author":"A Fern\u00e1ndez","year":"2018","unstructured":"Fern\u00e1ndez, A., Garc\u00eda, S., Galar, M., Prati, R.C., Krawczyk, B., Herrera, F.: Learning from Imbalanced Data Sets, vol. 11. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-98074-4"},{"key":"51_CR11","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1007\/978-3-030-47436-2_7","volume-title":"Advances in Knowledge Discovery and Data Mining","author":"J Kong","year":"2020","unstructured":"Kong, J., Rios, T., Kowalczyk, W., Menzel, S., B\u00e4ck, T.: On the performance of oversampling techniques for class imbalance problems. In: Lauw, H.W., Wong, R.C.-W., Ntoulas, A., Lim, E.-P., Ng, S.-K., Pan, S.J. (eds.) PAKDD 2020. LNCS (LNAI), vol. 12085, pp. 84\u201396. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-47436-2_7"},{"key":"51_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.116962","volume":"199","author":"M Lango","year":"2022","unstructured":"Lango, M., Stefanowski, J.: What makes multi-class imbalanced problems difficult? An experimental study. Expert Syst. Appl. 199, 116962 (2022)","journal-title":"Expert Syst. Appl."},{"key":"51_CR13","unstructured":"Mottini, A., Lheritier, A., Acuna-Agost, R.: Airline passenger name record generation using generative adversarial networks. arXiv preprint arXiv:1807.06657 (2018)"},{"key":"51_CR14","doi-asserted-by":"crossref","unstructured":"Park, N., Mohammadi, M., Gorde, K., Jajodia, S., Park, H., Kim, Y.: Data synthesis based on generative adversarial networks. arXiv preprint arXiv:1806.03384 (2018)","DOI":"10.14778\/3231751.3231757"},{"key":"51_CR15","series-title":"Communications in Computer and Information Science","doi-asserted-by":"publisher","first-page":"386","DOI":"10.1007\/978-3-031-08223-8_32","volume-title":"Engineering Applications of Neural Networks","author":"PK Saha","year":"2022","unstructured":"Saha, P.K., Logofatu, D.: Efficient approaches for data augmentation by using generative adversarial networks. In: Iliadis, L., Jayne, C., Tefas, A., Pimenidis, E. (eds.) EANN 2022. CCIS, vol. 1600, pp. 386\u2013399. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-08223-8_32"},{"issue":"4","key":"51_CR16","doi-asserted-by":"publisher","first-page":"571","DOI":"10.1080\/0952813X.2021.1907795","volume":"34","author":"A Singh","year":"2022","unstructured":"Singh, A., Ranjan, R.K., Tiwari, A.: Credit card fraud detection under extreme imbalanced data: a comparative study of data-level algorithms. J. Exp. Theor. Artif. Intell. 34(4), 571\u2013598 (2022)","journal-title":"J. Exp. Theor. Artif. Intell."},{"key":"51_CR17","doi-asserted-by":"publisher","first-page":"18450","DOI":"10.1109\/ACCESS.2019.2896409","volume":"7","author":"Q Wang","year":"2019","unstructured":"Wang, Q., et al.: WGAN-based synthetic minority over-sampling technique: improving semantic fine-grained classification for lung nodules in CT images. IEEE Access 7, 18450\u201318463 (2019)","journal-title":"IEEE Access"},{"issue":"1","key":"51_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13634-022-00871-6","volume":"2022","author":"T Wu","year":"2022","unstructured":"Wu, T., Fan, H., Zhu, H., You, C., Zhou, H., Huang, X.: Intrusion detection system combined enhanced random forest with smote algorithm. EURASIP J. Adv. Signal Process. 2022(1), 1\u201320 (2022)","journal-title":"EURASIP J. Adv. Signal Process."},{"key":"51_CR19","unstructured":"Xu, L., Skoularidou, M., Cuesta-Infante, A., Veeramachaneni, K.: Modeling tabular data using conditional GAN. In: Advances in Neural Information Processing Systems 32 (2019)"},{"key":"51_CR20","doi-asserted-by":"publisher","first-page":"2247","DOI":"10.1007\/s40747-021-00638-w","volume":"8","author":"X Yi","year":"2022","unstructured":"Yi, X., Xu, Y., Hu, Q., Krishnamoorthy, S., Li, W., Tang, Z.: ASN-SMOTE: a synthetic minority oversampling method with adaptive qualified synthesizer selection. Complex Intell. Syst. 8, 2247\u20132272 (2022)","journal-title":"Complex Intell. Syst."}],"container-title":["Lecture Notes in Computer Science","Intelligent Data Engineering and Automated Learning \u2013 IDEAL 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-21753-1_51","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T12:18:28Z","timestamp":1710332308000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-21753-1_51"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031217524","9783031217531"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-21753-1_51","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"21 November 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IDEAL","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Data Engineering and Automated Learning","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Manchester","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 November 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 November 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ideal2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ideal-conf.com\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"79","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"52","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"66% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.9","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.1","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}