{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T06:59:35Z","timestamp":1760597975143,"version":"3.37.3"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2020,6,22]],"date-time":"2020-06-22T00:00:00Z","timestamp":1592784000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,6,22]],"date-time":"2020-06-22T00:00:00Z","timestamp":1592784000000},"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":["Appl Intell"],"published-print":{"date-parts":[[2020,11]]},"DOI":"10.1007\/s10489-020-01719-y","type":"journal-article","created":{"date-parts":[[2020,6,22]],"date-time":"2020-06-22T06:02:26Z","timestamp":1592805746000},"page":"3678-3694","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Large margin classifiers to generate synthetic data for imbalanced datasets"],"prefix":"10.1007","volume":"50","author":[{"given":"Marcelo","family":"Ladeira Marques","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5958-4766","authenticated-orcid":false,"given":"Saulo","family":"Moraes Villela","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7413-2880","authenticated-orcid":false,"given":"Carlos Cristiano","family":"Hasenclever Borges","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,6,22]]},"reference":[{"key":"1719_CR1","doi-asserted-by":"crossref","unstructured":"Marsland S (2015) Machine learning: an algorithmic perspective. CRC press, Boca Raton","DOI":"10.1201\/b17476"},{"issue":"4","key":"1719_CR2","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1023\/A:1009744630224","volume":"2","author":"SK Murthy","year":"1998","unstructured":"Murthy S K (1998) Automatic construction of decision trees from data: a multi-disciplinary survey. Data Min Knowl Discov 2(4):345\u2013389","journal-title":"Data Min Knowl Discov"},{"key":"1719_CR3","doi-asserted-by":"crossref","unstructured":"Rosenblatt F (1962) Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms. Spartan","DOI":"10.21236\/AD0256582"},{"key":"1719_CR4","doi-asserted-by":"crossref","unstructured":"Rumelhart D E, Hinton G E, Williams R J (1985) Learning internal representations by error propagation. Technical report, California Univ San Diego La Jolla Inst for Cognitive Science","DOI":"10.21236\/ADA164453"},{"key":"1719_CR5","unstructured":"Howlett RJ, Jain LC (2013) Radial basis function networks 2: new advances in design, vol 67. Physica"},{"issue":"3","key":"1719_CR6","first-page":"273","volume":"20","author":"C Cortes","year":"1995","unstructured":"Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273\u2013297","journal-title":"Mach Learn"},{"issue":"1","key":"1719_CR7","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1007730.1007733","volume":"6","author":"NV Chawla","year":"2004","unstructured":"Chawla N V, Japkowicz N, Kotcz A (2004) Special issue on learning from imbalanced data sets. ACM Sigkdd Explor Newslett 6(1):1\u20136","journal-title":"ACM Sigkdd Explor Newslett"},{"issue":"4","key":"1719_CR8","first-page":"42","volume":"2","author":"V Ganganwar","year":"2012","unstructured":"Ganganwar V (2012) An overview of classification algorithms for imbalanced datasets. Int J Emerg Technol Adv Eng 2(4):42\u201347","journal-title":"Int J Emerg Technol Adv Eng"},{"key":"1719_CR9","unstructured":"Liu A, Ghosh J, Martin CE (2007) Generative oversampling for mining imbalanced datasets. In: DMIN"},{"key":"1719_CR10","unstructured":"Chan P K, Stolfo S J (1998) Toward scalable learning with non-uniform class and cost distributions: a case study in credit card fraud detection. In: KDD"},{"issue":"1","key":"1719_CR11","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1145\/1007730.1007738","volume":"6","author":"C Phua","year":"2004","unstructured":"Phua C, Alahakoon D, Lee V (2004) Minority report in fraud detection: classification of skewed data. Acm sigkdd Explor Newslett 6(1):50\u201359","journal-title":"Acm sigkdd Explor Newslett"},{"key":"1719_CR12","unstructured":"Kubat M, Holte RC, Matwin S (1998) Machine learning for the detection of oil spills in satellite radar images. ML"},{"key":"1719_CR13","doi-asserted-by":"crossref","unstructured":"Sun Y, Kamel M S, Wong AKC, Wang Y (2007) Cost-sensitive boosting for classification of imbalanced data. PR","DOI":"10.1016\/j.patcog.2007.04.009"},{"issue":"2","key":"1719_CR14","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1016\/j.neunet.2007.12.031","volume":"21","author":"MA Mazurowski","year":"2008","unstructured":"Mazurowski M A, Habas P A, Zurada J M, Lo J Y, Baker J A, Tourassi G D (2008) Training neural network classifiers for medical decision making: The effects of imbalanced datasets on classification performance. Neural Netw 21(2):427\u2013436","journal-title":"Neural Netw"},{"key":"1719_CR15","first-page":"533","volume":"16","author":"RM Everson","year":"2006","unstructured":"Everson R M, Fieldsend J E (2006) Multi-objective optimisation for receiver operating characteristic analysis. Multi-Object Mach Learn 16:533\u2013556","journal-title":"Multi-Object Mach Learn"},{"issue":"1","key":"1719_CR16","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1145\/1007730.1007739","volume":"6","author":"B Raskutti","year":"2004","unstructured":"Raskutti B, Kowalczyk A (2004) Extreme re-balancing for svms: a case study. ACM Sigkdd Explor Newslett 6(1):60\u201369","journal-title":"ACM Sigkdd Explor Newslett"},{"issue":"7","key":"1719_CR17","doi-asserted-by":"crossref","first-page":"1466","DOI":"10.1016\/j.neucom.2006.05.013","volume":"70","author":"L Manevitz","year":"2007","unstructured":"Manevitz L, Yousef M (2007) One-class document classification via neural networks. Neurocomputing 70 (7):1466\u20131481","journal-title":"Neurocomputing"},{"key":"1719_CR18","doi-asserted-by":"crossref","unstructured":"Tian J, Gu H, Liu W (2011) Imbalanced classification using support vector machine ensemble. NCA","DOI":"10.1007\/s00521-010-0349-9"},{"key":"1719_CR19","unstructured":"N\u00fa\u00f1ez Castro H, Gonz\u00e1lez Abril L, Angulo Bah\u00f3n C (2011) A post-processing strategy for svm learning from unbalanced data. In: 19th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, pp 195\u2013200"},{"key":"1719_CR20","doi-asserted-by":"crossref","unstructured":"Tao X, Ji H, Xie Y (2007) A modified psvm and its application to unbalanced data classification. In: ICNC","DOI":"10.1109\/ICNC.2007.68"},{"key":"1719_CR21","doi-asserted-by":"crossref","unstructured":"Yen S, Lee Y (2009) Cluster-based under-sampling approaches for imbalanced data distributions. ESA","DOI":"10.1016\/j.eswa.2008.06.108"},{"key":"1719_CR22","doi-asserted-by":"crossref","unstructured":"Han H, Wang W, Mao B (2005) Borderline-smote: a new over-sampling method in imbalanced data sets learning. In: ICIC","DOI":"10.1007\/11538059_91"},{"key":"1719_CR23","doi-asserted-by":"crossref","unstructured":"Torres LCB, FCFSTAB CL Castro (2015) Distance-based large margin classifier suitable for integrated circuit implementation. Electron Lett 51(2):1967\u20131969","DOI":"10.1049\/el.2015.1644"},{"issue":"2","key":"1719_CR24","doi-asserted-by":"crossref","first-page":"2597","DOI":"10.3233\/JIFS-18426","volume":"35","author":"P Vitor de Campos Souza","year":"2018","unstructured":"Vitor de Campos Souza P (2018) Pruning fuzzy neural networks based on unineuron for problems of classification of patterns. J Intell Fuzzy Syst 35(2):2597\u20132605","journal-title":"J Intell Fuzzy Syst"},{"key":"1719_CR25","doi-asserted-by":"crossref","unstructured":"Zhang X, Fu Y, Zang A, Sigal L, Agam G (2015) Learning classifiers from synthetic data using a multichannel autoencoder. arXiv","DOI":"10.1109\/ICMLA.2015.199"},{"key":"1719_CR26","doi-asserted-by":"crossref","unstructured":"Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP (2002) SMOTE: Synthetic Minority Over-sampling Technique. JAIR","DOI":"10.1613\/jair.953"},{"key":"1719_CR27","unstructured":"He H, Bai Y, Garcia EA, Li S (2008) Adasyn: Adaptive synthetic sampling approach for imbalanced learning. In: 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), pp 1322\u20131328"},{"key":"1719_CR28","doi-asserted-by":"crossref","unstructured":"Barua S, Islam M M, Yao X, Murase K (2014) Mwmote\u2013majority weighted minority oversampling technique for imbalanced data set learning. TKDE","DOI":"10.1109\/TKDE.2012.232"},{"key":"1719_CR29","doi-asserted-by":"crossref","unstructured":"Koto F (2014) Smote-out, smote-cosine and selected-smote: an enhancement strategy to handle imbalance in data level. In: ICACSIS","DOI":"10.1109\/ICACSIS.2014.7065849"},{"issue":"5","key":"1719_CR30","first-page":"1651","volume":"26","author":"RE Schapire","year":"1998","unstructured":"Schapire R E, Freund Y, Bartlett P, Lee W S (1998) Boosting the margin: a new explanation for the effectiveness of voting methods. Ann Statist 26(5):1651\u20131686","journal-title":"Ann Statist"},{"key":"1719_CR31","doi-asserted-by":"crossref","first-page":"815","DOI":"10.3390\/app8050815","volume":"8","author":"W Feng","year":"2018","unstructured":"Feng W, Huang W, Ren J (2018) Class imbalance ensemble learning based on the margin theory. Appl Sci 8:815\u2013842","journal-title":"Appl Sci"},{"key":"1719_CR32","doi-asserted-by":"crossref","unstructured":"Wang Q, Luo Z, Huang J, Feng Y, Liu Z (2017) A novel ensemble method for imbalanced data learning: Bagging of extrapolation-smote svm. Computational Intelligence and Neuroscience","DOI":"10.1155\/2017\/1827016"},{"key":"1719_CR33","first-page":"19","volume":"44","author":"C Jingnian","year":"2018","unstructured":"Jingnian C, Shunxiang H, Li X (2018) Speeding up algorithm for support vector machine based on alien neighbor. Comput Eng 44:19\u201324","journal-title":"Comput Eng"},{"key":"1719_CR34","doi-asserted-by":"crossref","unstructured":"Xie W, Liang G, Dong Z, Tan B, Zhang B (2019) An improved oversampling algorithm based on the samples\u2019 selection strategy for classifying imbalanced data. Mathematical Problems in Engineering","DOI":"10.1155\/2019\/3526539"},{"key":"1719_CR35","doi-asserted-by":"crossref","unstructured":"Attenbert J, Ertekin S (2013) Class imbalance and active learning. In: Imbalanced learning: foundations, Algorithms, and Applications. Wiley, pp 101\u2013150","DOI":"10.1002\/9781118646106.ch6"},{"key":"1719_CR36","doi-asserted-by":"crossref","unstructured":"Boser BE, Guyon IM, Vapnik VN (1992) A training algorithm for optimal margin classifiers. In: Proceedings of the fifth annual workshop on Computational learning theory. ACM, pp 144\u2013152","DOI":"10.1145\/130385.130401"},{"key":"1719_CR37","doi-asserted-by":"crossref","first-page":"216","DOI":"10.1016\/j.patcog.2016.01.016","volume":"55","author":"SM Villela","year":"2016","unstructured":"Villela S M, Leite S C, Fonseca Neto R (2016) Incremental p-margin algorithm for classification with arbitrary norm. Pattern Recogn 55:216\u2013272","journal-title":"Pattern Recogn"},{"key":"1719_CR38","doi-asserted-by":"crossref","unstructured":"Fern\u00e1ndez A, Garcia S, Herrera F, Chawla N (2018) SMOTE for learning from imbalanced data: Progress and challenges, marking the 15-year anniversary. J Artif Intell Res 61:863\u2013905","DOI":"10.1613\/jair.1.11192"},{"key":"1719_CR39","unstructured":"Bache K, Lichman M (2013) UCI machine learning repository. http:\/\/archive.ics.uci.edu\/ml"},{"issue":"5439","key":"1719_CR40","doi-asserted-by":"crossref","first-page":"531","DOI":"10.1126\/science.286.5439.531","volume":"286","author":"TR Golub","year":"1999","unstructured":"Golub T R, Slonim D K, Tamayo P, Huard C, Gaasenbeek M, Mesirov J P, Coller H, Loh M L, Downing J R, Caligiuri M A, Bloomfield C D, Lander E S (1999) Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286(5439):531\u2013537","journal-title":"Science"},{"issue":"12","key":"1719_CR41","doi-asserted-by":"crossref","first-page":"6745","DOI":"10.1073\/pnas.96.12.6745","volume":"96","author":"U Alon","year":"1999","unstructured":"Alon U, Barkai N, Notterman D A, Gish K, Ybarra S, Mack D, Levine A J (1999) Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Proc Natl Acad Sci U S A 96(12):6745\u20136750","journal-title":"Proc Natl Acad Sci U S A"},{"key":"1719_CR42","unstructured":"S\u00e1nchez R L, Alcal\u00e1 F J, Fern\u00e1ndez H A, Luengo M J, Derrac R J, Garc\u00eda LS, Herrera TF (2011) Keel data-mining software tool: Data set repository, integration of algorithms and experimental analysis framework. JMLSC"},{"key":"1719_CR43","first-page":"83","volume":"105662","author":"G Kov\u00e1cs","year":"2019","unstructured":"Kov\u00e1cs G (2019) An empirical comparison and evaluation of minority oversampling techniques on a large number of imbalanced datasets. Appl Soft Comput 105662:83","journal-title":"Appl Soft Comput"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-020-01719-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-020-01719-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-020-01719-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,6,21]],"date-time":"2021-06-21T23:59:53Z","timestamp":1624319993000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-020-01719-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,6,22]]},"references-count":43,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2020,11]]}},"alternative-id":["1719"],"URL":"https:\/\/doi.org\/10.1007\/s10489-020-01719-y","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"type":"print","value":"0924-669X"},{"type":"electronic","value":"1573-7497"}],"subject":[],"published":{"date-parts":[[2020,6,22]]},"assertion":[{"value":"22 June 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}