{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T07:27:00Z","timestamp":1767338820689,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":15,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819967018"},{"type":"electronic","value":"9789819967025"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-981-99-6702-5_3","type":"book-chapter","created":{"date-parts":[[2023,11,20]],"date-time":"2023-11-20T18:02:42Z","timestamp":1700503362000},"page":"35-48","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Empirical Review of Oversampling Methods to Handle the Class Imbalance Problem"],"prefix":"10.1007","author":[{"given":"Ritika","family":"Kumari","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jaspreeti","family":"Singh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anjana","family":"Gosain","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,11,21]]},"reference":[{"key":"3_CR1","doi-asserted-by":"crossref","unstructured":"Gosain, A., Sardana, S.: Handling class imbalance problem using oversampling techniques: a review. In: International Conference on Advances in Computing, Communications and Informatics, pp. 79\u201385. IEEE (2017)","DOI":"10.1109\/ICACCI.2017.8125820"},{"key":"3_CR2","doi-asserted-by":"crossref","unstructured":"Nishant, P.S., Rohit, B., Chandra, B.S., Mehrotra, S.: HOUSEN: hybrid over\u2013undersampling and ensemble approach for imbalance classification. In: Inventive Systems and Control, pp. 93\u2013108. Springer, Singapore (2021)","DOI":"10.1007\/978-981-16-1395-1_8"},{"issue":"7","key":"3_CR3","doi-asserted-by":"publisher","first-page":"2839","DOI":"10.1007\/s00521-020-05130-z","volume":"33","author":"E Elyan","year":"2021","unstructured":"E Elyan CF Moreno-Garcia C Jayne 2021 CDSMOTE: class decomposition and synthetic minority class oversampling technique for imbalanced-data classification Neural Comput. Appl. 33 7 2839 2851","journal-title":"Neural Comput. Appl."},{"issue":"4","key":"3_CR4","doi-asserted-by":"publisher","first-page":"3853","DOI":"10.1007\/s13369-021-05347-7","volume":"46","author":"AS Desuky","year":"2021","unstructured":"AS Desuky S Hussain 2021 An improved hybrid approach for handling class imbalance problem Arab. J. Sci. Eng. 46 4 3853 3864","journal-title":"Arab. J. Sci. Eng."},{"key":"3_CR5","doi-asserted-by":"crossref","unstructured":"Kaur, P., Gosain, A.: Comparing the behavior of oversampling and undersampling approach of class imbalance learning by combining class imbalance problem with noise. In: ICT Based Innovations, pp. 23\u201330. Springer, Singapore (2018)","DOI":"10.1007\/978-981-10-6602-3_3"},{"key":"3_CR6","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"NV Chawla KW Bowyer LO Hall WP Kegelmeyer 2002 SMOTE: synthetic minority over-sampling technique J. Artif. Intell. Res. 16 321 357","journal-title":"J. Artif. Intell. Res."},{"issue":"2","key":"3_CR7","doi-asserted-by":"publisher","first-page":"194","DOI":"10.3390\/sym13020194","volume":"13","author":"Z Jiang","year":"2021","unstructured":"Z Jiang T Pan C Zhang J Yang 2021 A new oversampling method based on the classification contribution degree Symmetry 13 2 194","journal-title":"Symmetry"},{"key":"3_CR8","doi-asserted-by":"crossref","unstructured":"Dong, Y., Wang, X.: A new over-sampling approach: random-SMOTE for learning from imbalanced data sets. In: International Conference on Knowledge Science, Engineering and Management, pp. 343\u2013352. Springer, Berlin (2011)","DOI":"10.1007\/978-3-642-25975-3_30"},{"key":"3_CR9","unstructured":"De La Calleja, J., Fuentes, O.: A distance-based over-sampling method for learning from imbalanced data sets. In: FLAIRS Conference, pp. 634\u2013635 (2007)"},{"key":"3_CR10","doi-asserted-by":"crossref","unstructured":"Han, H., Wang, W.Y., Mao, B.H.: Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning. In: International Conference on Intelligent Computing, pp. 878\u2013887. Springer, Berlin (2005)","DOI":"10.1007\/11538059_91"},{"key":"3_CR11","doi-asserted-by":"crossref","unstructured":"Gazzah, S., Amara, N.E.B.: New oversampling approaches based on polynomial fitting for imbalanced data sets. In: 2008 the Eighth IAPR International Workshop on Document Analysis Systems, pp. 677\u2013684. IEEE (2008)","DOI":"10.1109\/DAS.2008.74"},{"key":"3_CR12","doi-asserted-by":"crossref","unstructured":"Hu, S., Liang, Y., Ma, L., He, Y.: MSMOTE: improving classification performance when training data is imbalanced. In: Second International Workshop on Computer Science and Engineering, vol. 2, pp. 13\u201317. IEEE (2009)","DOI":"10.1109\/WCSE.2009.756"},{"issue":"4","key":"3_CR13","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1007\/s42044-020-00058-y","volume":"3","author":"AK Verma","year":"2020","unstructured":"AK Verma S Pal BB Tiwari 2020 Skin disease prediction using ensemble methods and a new hybrid feature selection technique Iran J. Comput. Sci. 3 4 207 216","journal-title":"Iran J. Comput. Sci."},{"issue":"1","key":"3_CR14","doi-asserted-by":"publisher","first-page":"1249","DOI":"10.1007\/s12652-020-02167-9","volume":"12","author":"A Thakkar","year":"2021","unstructured":"A Thakkar R Lohiya 2021 Attack classification using feature selection techniques: a comparative study J. Ambient Intell. Humaniz. Comput. 12 1 1249 1266","journal-title":"J. Ambient Intell. Humaniz. Comput."},{"key":"3_CR15","doi-asserted-by":"crossref","unstructured":"Gosain, A., Sardana, S.: Farthest SMOTE: a modified SMOTE approach. In: Computational Intelligence in Data Mining, pp. 309\u2013320. Springer, Singapore (2019)","DOI":"10.1007\/978-981-10-8055-5_28"}],"container-title":["Smart Innovation, Systems and Technologies","Evolution in Computational Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-6702-5_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,9]],"date-time":"2024-10-09T06:06:10Z","timestamp":1728453970000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-6702-5_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9789819967018","9789819967025"],"references-count":15,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-6702-5_3","relation":{},"ISSN":["2190-3018","2190-3026"],"issn-type":[{"type":"print","value":"2190-3018"},{"type":"electronic","value":"2190-3026"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"21 November 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"FICTA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Frontiers of Intelligent Computing: Theory and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Cardiff","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":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 April 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 April 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ficta2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ficta.co.uk\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}