{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T07:14:44Z","timestamp":1774682084185,"version":"3.50.1"},"reference-count":29,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2022,6,18]],"date-time":"2022-06-18T00:00:00Z","timestamp":1655510400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,6,18]],"date-time":"2022-06-18T00:00:00Z","timestamp":1655510400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/100007219","name":"Natural Science Foundation of Shanghai","doi-asserted-by":"publisher","award":["19ZR1415700"],"award-info":[{"award-number":["19ZR1415700"]}],"id":[{"id":"10.13039\/100007219","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61902127"],"award-info":[{"award-number":["61902127"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Data Min Knowl Disc"],"published-print":{"date-parts":[[2022,9]]},"DOI":"10.1007\/s10618-022-00838-z","type":"journal-article","created":{"date-parts":[[2022,6,18]],"date-time":"2022-06-18T06:02:33Z","timestamp":1655532153000},"page":"1601-1622","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Dynamic self-paced sampling ensemble for highly imbalanced and class-overlapped data classification"],"prefix":"10.1007","volume":"36","author":[{"given":"Fang","family":"Zhou","sequence":"first","affiliation":[]},{"given":"Suting","family":"Gao","sequence":"additional","affiliation":[]},{"given":"Lyu","family":"Ni","sequence":"additional","affiliation":[]},{"given":"Martin","family":"Pavlovski","sequence":"additional","affiliation":[]},{"given":"Qiwen","family":"Dong","sequence":"additional","affiliation":[]},{"given":"Zoran","family":"Obradovic","sequence":"additional","affiliation":[]},{"given":"Weining","family":"Qian","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,6,18]]},"reference":[{"key":"838_CR1","unstructured":"Asuncion A, Newman D (2007) UCI machine learning repository"},{"key":"838_CR2","unstructured":"Cao K, Wei C, Gaidon A, Arechiga N, Ma T (2019) Learning imbalanced datasets with label-distribution-aware margin loss. In: Proceedings of the 33rd international conference on neural information processing systems, pp 1567\u20131578"},{"key":"838_CR3","doi-asserted-by":"crossref","unstructured":"Chawla NV, Lazarevic A, Hall LO, Bowyer KW (2003) SMOTEBoost: improving prediction of the minority class in boosting. In: European conference on principles of data mining and knowledge discovery. Springer, pp 107\u2013119","DOI":"10.1007\/978-3-540-39804-2_12"},{"key":"838_CR4","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP (2002) SMOTE: synthetic minority over-sampling technique. J Artif Intell Res 16:321\u2013357","journal-title":"J Artif Intell Res"},{"key":"838_CR5","unstructured":"Chen T, He T, Benesty M, Khotilovich V, Tang Y (2015) Xgboost: extreme gradient boosting. R package version 0.4-2, pp 1\u20134"},{"key":"838_CR6","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1006\/jcss.1997.1504","volume":"55","author":"Y Freund","year":"1997","unstructured":"Freund Y, Schapire RE (1997) A decision-theoretic generalization of on-line learning and an application to boosting. J Comput Syst Sci 55:119\u2013139","journal-title":"J Comput Syst Sci"},{"key":"838_CR7","first-page":"367","volume":"38","author":"JH Friedman","year":"2002","unstructured":"Friedman JH (2002) Stochastic gradient boosting. Comput Stat 38:367\u2013378","journal-title":"Comput Stat"},{"key":"838_CR8","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1016\/j.patcog.2017.04.028","volume":"70","author":"S G\u00f3nzalez","year":"2017","unstructured":"G\u00f3nzalez S, Garcia S, L\u00e1zaro M, Figueiras-Vidal AR, Herrera F (2017) Class switching according to nearest enemy distance for learning from highly imbalanced data-sets. Pattern Recognit 70:12\u201324","journal-title":"Pattern Recognit"},{"key":"838_CR9","unstructured":"He H, Bai Y, Garcia EA, Li S (2008) ADASYN: adaptive synthetic sampling approach for imbalanced learning. In: IEEE international joint conference on neural networks, pp 1322\u20131328"},{"key":"838_CR10","unstructured":"Kumar MP, Packer B, Koller D (2010) Self-paced learning for latent variable models. In: NIPS, pp 1189\u20131197"},{"key":"838_CR11","unstructured":"Last F, Douzas G, Bacao F (2017) Oversampling for imbalanced learning based on k-means and smote. arXiv preprint arXiv:1711.00837"},{"key":"838_CR12","doi-asserted-by":"crossref","unstructured":"Liu XY, Wu J, Zhou ZH (2008) Exploratory undersampling for class-imbalance learning. IEEE Trans Syst Man Cybern 539\u2013550","DOI":"10.1109\/TSMCB.2008.2007853"},{"key":"838_CR13","doi-asserted-by":"crossref","unstructured":"Liu XY, Zhou ZH (2006) The influence of class imbalance on cost-sensitive learning: an empirical study. In: International conference on data mining. IEEE, pp 970\u2013974","DOI":"10.1109\/ICDM.2006.158"},{"key":"838_CR14","doi-asserted-by":"crossref","unstructured":"Liu Z, Cao W, Gao Z, Bian J, Chen H, Chang Y, Liu T (2020) Self-paced ensemble for highly imbalanced massive data classification. In: IEEE 36th international conference on data engineering, pp 841\u2013852","DOI":"10.1109\/ICDE48307.2020.00078"},{"key":"838_CR15","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1007\/s12293-017-0236-3","volume":"11","author":"C Lu","year":"2019","unstructured":"Lu C, Ke H, Zhang G, Mei Y, Xu H (2019) An improved weighted extreme learning machine for imbalanced data classification. Memetic Comput 11:27\u201334","journal-title":"Memetic Comput"},{"key":"838_CR16","doi-asserted-by":"publisher","first-page":"232","DOI":"10.1016\/j.patcog.2019.01.036","volume":"90","author":"R O\u2019Brien","year":"2019","unstructured":"O\u2019Brien R, Ishwaran H (2019) A random forests quantile classifier for class imbalanced data. Pattern Recogn 90:232\u2013249","journal-title":"Pattern Recogn"},{"key":"838_CR17","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V et al (2011) Scikit-learn: machine learning in python. J Mach Learn Res 12:2825\u20132830","journal-title":"J Mach Learn Res"},{"key":"838_CR18","doi-asserted-by":"crossref","unstructured":"Peng M, Zhang Q, Xing X, Gui T, Huang X, Jiang YG, Ding K, Chen Z (2019) Trainable undersampling for class-imbalance learning. In: Proceedings of the AAAI conference on artificial intelligence, pp 4707\u20134714","DOI":"10.1609\/aaai.v33i01.33014707"},{"key":"838_CR19","doi-asserted-by":"crossref","first-page":"3784","DOI":"10.1109\/TNNLS.2017.2736643","volume":"29","author":"AD Pozzolo","year":"2017","unstructured":"Pozzolo AD, Boracchi G, Caelen O, Alippi C, Bontempi G (2017) Credit card fraud detection: a realistic modeling and a novel learning strategy. IEEE Trans Neural Netw Learn Syst 29:3784\u20133797","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"838_CR20","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1109\/TSMCA.2009.2029559","volume":"40","author":"C Seiffert","year":"2009","unstructured":"Seiffert C, Khoshgoftaar TM, Van HJ, Napolitano A (2009) RUSBoost: a hybrid approach to alleviating class imbalance. IEEE Trans Syst Man Cybern Part A Syst Hum 40:185\u2013197","journal-title":"IEEE Trans Syst Man Cybern Part A Syst Hum"},{"key":"838_CR21","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1016\/j.ins.2019.08.062","volume":"509","author":"P Vuttipittayamongkol","year":"2020","unstructured":"Vuttipittayamongkol P, Elyan E (2020) Neighbourhood-based undersampling approach for handling imbalanced and overlapped data. Inf Sci 509:47\u201370","journal-title":"Inf Sci"},{"key":"838_CR22","doi-asserted-by":"crossref","unstructured":"Vuttipittayamongkol P, Elyan E (2020b) Overlap-based undersampling method for classification of imbalanced medical datasets. In: IFIP international conference on artificial intelligence applications and innovations. Springer, pp 358\u2013369","DOI":"10.1007\/978-3-030-49186-4_30"},{"key":"838_CR23","doi-asserted-by":"crossref","unstructured":"Vuttipittayamongkol P, Elyan E, Petrovski A, Jayne C (2018) Overlap-based undersampling for improving imbalanced data classification. In: International conference on intelligent data engineering and automated learning. Springer, pp 689\u2013697","DOI":"10.1007\/978-3-030-03493-1_72"},{"key":"838_CR24","doi-asserted-by":"crossref","unstructured":"Wallace BC, Small K, Brodley C, Trikalinos TA (2011) Class imbalance, redux. In: 2011 IEEE 11th international conference on data mining. IEEE, pp 754\u2013763","DOI":"10.1109\/ICDM.2011.33"},{"key":"838_CR25","doi-asserted-by":"crossref","unstructured":"Wang Y, Gan W, Yang J, Wu W, Yan J (2019) Dynamic curriculum learning for imbalanced data classification. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 5017\u20135026","DOI":"10.1109\/ICCV.2019.00512"},{"key":"838_CR26","doi-asserted-by":"crossref","unstructured":"Wang S, Yao X (2009)Diversity analysis on imbalanced data sets by using ensemble models. In: 2009 IEEE symposium on computational intelligence and data mining. IEEE, pp 324\u2013331","DOI":"10.1109\/CIDM.2009.4938667"},{"key":"838_CR27","doi-asserted-by":"crossref","unstructured":"Wei W, Li J, Cao L, Ou Y, Chen J (2013) Effective detection of sophisticated online banking fraud on extremely imbalanced data. WWW, pp 449\u2013475","DOI":"10.1007\/s11280-012-0178-0"},{"key":"838_CR28","doi-asserted-by":"crossref","unstructured":"Wu F, Jing XY, Shan S, Zuo W, Yang JY (2017) Multiset feature learning for highly imbalanced data classification. In: Proceedings of the AAAI conference on artificial intelligence, vol\u00a031","DOI":"10.1609\/aaai.v31i1.10739"},{"key":"838_CR29","doi-asserted-by":"publisher","first-page":"160","DOI":"10.1016\/j.patcog.2017.12.017","volume":"77","author":"X Yuan","year":"2018","unstructured":"Yuan X, Xie L, Abouelenien M (2018) A regularized ensemble framework of deep learning for cancer detection from multi-class, imbalanced training data. Pattern Recogn 77:160\u2013172","journal-title":"Pattern Recogn"}],"container-title":["Data Mining and Knowledge Discovery"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10618-022-00838-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10618-022-00838-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10618-022-00838-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,27]],"date-time":"2024-09-27T04:51:28Z","timestamp":1727412688000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10618-022-00838-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,18]]},"references-count":29,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2022,9]]}},"alternative-id":["838"],"URL":"https:\/\/doi.org\/10.1007\/s10618-022-00838-z","relation":{},"ISSN":["1384-5810","1573-756X"],"issn-type":[{"value":"1384-5810","type":"print"},{"value":"1573-756X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,18]]},"assertion":[{"value":"7 October 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 April 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 June 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}