{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,9]],"date-time":"2025-07-09T22:45:50Z","timestamp":1752101150865,"version":"3.40.3"},"publisher-location":"Cham","reference-count":13,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031683220"},{"type":"electronic","value":"9783031683237"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-68323-7_9","type":"book-chapter","created":{"date-parts":[[2024,8,17]],"date-time":"2024-08-17T07:02:18Z","timestamp":1723878138000},"page":"112-119","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Incremental SMOTE with\u00a0Control Coefficient for\u00a0Classifiers in\u00a0Data Starved Medical Applications"],"prefix":"10.1007","author":[{"given":"Wan D.","family":"Bae","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shayma","family":"Alkobaisi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Siddheshwari","family":"Bankar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sartaj","family":"Bhuvaji","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jay","family":"Singhvi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Madhuroopa","family":"Irukulla","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"William","family":"McDonnell","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,8,18]]},"reference":[{"key":"9_CR1","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., Kegelmeyer, W.P.: Smote: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321\u2013357 (2002)","journal-title":"J. Artif. Intell. Res."},{"key":"9_CR2","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1007\/978-3-540-39804-2_12","volume-title":"Knowledge Discovery in Databases: PKDD 2003","author":"NV Chawla","year":"2003","unstructured":"Chawla, N.V., Lazarevic, A., Hall, L.O., Bowyer, K.W.: SMOTEBoost: improving prediction of the minority class in boosting. In: Lavra\u010d, N., Gamberger, D., Todorovski, L., Blockeel, H. (eds.) PKDD 2003. LNCS (LNAI), vol. 2838, pp. 107\u2013119. Springer, Heidelberg (2003). https:\/\/doi.org\/10.1007\/978-3-540-39804-2_12"},{"key":"9_CR3","unstructured":"Gretel: Gretel. https:\/\/gretel.ai\/. Accessed 4 May 2024"},{"key":"9_CR4","doi-asserted-by":"crossref","unstructured":"Hoens, T.R., Chawla, N.V.: Imbalanced datasets: from sampling to classifiers. Foundations, Algorithms, and Applications. Wiley, Imbalanced Learning (2013)","DOI":"10.1002\/9781118646106.ch3"},{"key":"9_CR5","doi-asserted-by":"publisher","first-page":"106368","DOI":"10.1016\/j.knosys.2020.106368","volume":"207","author":"F Kamalov","year":"2020","unstructured":"Kamalov, F., Denisov, D.: Gamma distribution-based sampling for imbalanced data. Knowl.-Based Syst. 207, 106368 (2020)","journal-title":"Knowl.-Based Syst."},{"issue":"4","key":"9_CR6","doi-asserted-by":"publisher","first-page":"229","DOI":"10.5391\/IJFIS.2017.17.4.229","volume":"17","author":"H Lee","year":"2017","unstructured":"Lee, H., Kim, J., Kim, S.: Gaussian-based smote algorithm for solving skewed class distributions. Int. J. Fuzzy Logic Intell. Syst. 17(4), 229\u2013234 (2017)","journal-title":"Int. J. Fuzzy Logic Intell. Syst."},{"key":"9_CR7","unstructured":"MIT: The synthetic data vault. https:\/\/sdv.dev. Accessed 4 May 2024"},{"issue":"1","key":"9_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/2047-2501-2-3","volume":"2","author":"W Raghupathi","year":"2014","unstructured":"Raghupathi, W., Raghupathi, V.: Big data analytics in healthcare: promise and potential. Health Inf. Sci. Syst. 2(1), 1\u201310 (2014)","journal-title":"Health Inf. Sci. Syst."},{"key":"9_CR9","doi-asserted-by":"publisher","first-page":"117023","DOI":"10.1016\/j.eswa.2022.117023","volume":"200","author":"F Sa\u011flam","year":"2022","unstructured":"Sa\u011flam, F., Cengiz, M.A.: A novel smote-based resampling technique trough noise detection and the boosting procedure. Expert Syst. Appl. 200, 117023 (2022)","journal-title":"Expert Syst. Appl."},{"key":"9_CR10","doi-asserted-by":"publisher","first-page":"30655","DOI":"10.1109\/ACCESS.2022.3158977","volume":"10","author":"A Sharma","year":"2022","unstructured":"Sharma, A., Singh, P.K., Chandra, R.: SMOTified-GAN for class imbalanced pattern classification problems. Ieee Access 10, 30655\u201330665 (2022)","journal-title":"Ieee Access"},{"key":"9_CR11","doi-asserted-by":"crossref","unstructured":"Wan, Q., Deng, X., Li, M., Yang, H.: Sddsmote: synthetic minority oversampling technique based on sample density distribution for enhanced classification on imbalanced microarray data. In: The 6th International Conference on Compute and Data Analysis, pp. 35\u201342 (2022)","DOI":"10.1145\/3523089.3523096"},{"issue":"1","key":"9_CR12","doi-asserted-by":"publisher","first-page":"159","DOI":"10.3390\/app10010159","volume":"10","author":"J Woo","year":"2020","unstructured":"Woo, J., Rudasingwa, G., Kim, S.: Assessment of daily personal pm2. 5 exposure level according to four major activities among children. Appl. Sci. 10(1), 159 (2020)","journal-title":"Appl. Sci."},{"key":"9_CR13","unstructured":"Xu, L., Skoularidou, M., Cuesta-Infante, A., Veeramachaneni, K.: Modeling tabular data using conditional GAN. In: Advances in Neural Information Processing Systems, vol. 32 (2019)"}],"container-title":["Lecture Notes in Computer Science","Big Data Analytics and Knowledge Discovery"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-68323-7_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,17]],"date-time":"2024-08-17T07:03:19Z","timestamp":1723878199000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-68323-7_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031683220","9783031683237"],"references-count":13,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-68323-7_9","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"18 August 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DaWaK","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Big Data Analytics and Knowledge Discovery","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Naples","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","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":"26 August 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 August 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dawak2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.dexa.org\/dawak2024\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}