{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T04:12:57Z","timestamp":1772770377866,"version":"3.50.1"},"reference-count":68,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2019,5,8]],"date-time":"2019-05-08T00:00:00Z","timestamp":1557273600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["71571132"],"award-info":[{"award-number":["71571132"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. & Cyber."],"published-print":{"date-parts":[[2019,12]]},"DOI":"10.1007\/s13042-019-00953-2","type":"journal-article","created":{"date-parts":[[2019,5,8]],"date-time":"2019-05-08T10:02:54Z","timestamp":1557309774000},"page":"3687-3699","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":40,"title":["A Gaussian mixture model based combined resampling algorithm for classification of imbalanced credit data sets"],"prefix":"10.1007","volume":"10","author":[{"given":"Xu","family":"Han","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Runbang","family":"Cui","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanfei","family":"Lan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanzhe","family":"Kang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiang","family":"Deng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ning","family":"Jia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,5,8]]},"reference":[{"issue":"1","key":"953_CR1","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1007\/s13748-012-0034-6","volume":"2","author":"I Albisua","year":"2013","unstructured":"Albisua I, Arbelaitz O, Gurrutxaga I, Lasarguren A, Muguerza J, P\u00e9rez JM (2013) The quest for the optimal class distribution: an approach for enhancing the effectiveness of learning via resampling methods for imbalanced data sets. Prog Artif Intell 2(1):45\u201363","journal-title":"Prog Artif Intell"},{"issue":"3","key":"953_CR2","doi-asserted-by":"publisher","first-page":"505","DOI":"10.1016\/0378-4266(94)90007-8","volume":"18","author":"EI Altman","year":"2004","unstructured":"Altman EI, Marco G, Varetto F (2004) Corporate distress diagnosis: comparisons using linear discriminant analysis and neural networks (the Italian experience). J Bank Financ 18(3):505\u2013529","journal-title":"J Bank Financ"},{"issue":"2","key":"953_CR3","first-page":"293","volume":"12","author":"G Arminger","year":"1997","unstructured":"Arminger G, Enache D, Bonne T (1997) Analyzing credit risk data: a comparison of logistic discrimination, classification tree analysis, and feedforward networks. Comput Stat 12(2):293\u2013310","journal-title":"Comput Stat"},{"issue":"6","key":"953_CR4","doi-asserted-by":"publisher","first-page":"627","DOI":"10.1057\/palgrave.jors.2601545","volume":"54","author":"B Baesens","year":"2003","unstructured":"Baesens B, Gestel TV, Viaene S, Stepanova M, Suykens J, Vanthienen J (2003) Benchmarking state-of-the-art classification algorithms for credit scoring. J Oper Res Soc 54(6):627\u2013635","journal-title":"J Oper Res Soc"},{"issue":"1","key":"953_CR5","doi-asserted-by":"publisher","first-page":"S16","DOI":"10.1057\/jors.2008.171","volume":"60","author":"B Baesens","year":"2009","unstructured":"Baesens B, Mues C, Martens D, Vanthienen J (2009) 50\u00a0years of data mining and OR: upcoming trends and challenges. J Oper Res Soc 60(1):S16\u2013S23","journal-title":"J Oper Res Soc"},{"issue":"5","key":"953_CR6","doi-asserted-by":"publisher","first-page":"1653","DOI":"10.1016\/j.patcog.2014.10.032","volume":"48","author":"C Beyan","year":"2015","unstructured":"Beyan C, Fisher R (2015) Classifying imbalanced data sets using similarity based hierarchical decomposition. Pattern Recognit 48(5):1653\u20131672","journal-title":"Pattern Recognit"},{"key":"953_CR7","doi-asserted-by":"publisher","first-page":"529","DOI":"10.1016\/j.neucom.2014.07.064","volume":"150","author":"J B\u0142aszczy\u0144ski","year":"2015","unstructured":"B\u0142aszczy\u0144ski J, Stefanowski J (2015) Neighbourhood sampling in bagging for imbalanced data. Neurocomputing 150:529\u2013542","journal-title":"Neurocomputing"},{"issue":"3","key":"953_CR8","doi-asserted-by":"publisher","first-page":"3446","DOI":"10.1016\/j.eswa.2011.09.033","volume":"39","author":"I Brown","year":"2012","unstructured":"Brown I, Mues C (2012) An experimental comparison of classification algorithms for imbalanced credit scoring data sets. Expert Syst Appl 39(3):3446\u20133453","journal-title":"Expert Syst Appl"},{"key":"953_CR9","doi-asserted-by":"crossref","unstructured":"Chawla NV (2009) Data mining for imbalanced datasets: an overview. In:\u00a0Data mining and knowledge discovery handbook. Springer, Boston, MA, pp 875\u2013886","DOI":"10.1007\/978-0-387-09823-4_45"},{"issue":"1","key":"953_CR10","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(1):321\u2013357","journal-title":"J Artif Intell Res"},{"issue":"2","key":"953_CR11","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1007\/s10618-008-0087-0","volume":"17","author":"NV Chawla","year":"2008","unstructured":"Chawla NV, Cieslak DA, Hall LO, Joshi A (2008) Automatically countering imbalance and its empirical relationship to cost. Data Min Knowl Discov 17(2):225\u2013252","journal-title":"Data Min Knowl Discov"},{"issue":"1","key":"953_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/1007730.1007733","volume":"6","author":"NV Chawla","year":"2004","unstructured":"Chawla NV, Japkowicz N, Kotcz A (2004) Editorial: Special issue on learning from imbalanced data sets. ACM Sigkdd Explor Newsl 6(1):1\u20136","journal-title":"ACM Sigkdd Explor Newsl"},{"key":"953_CR13","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1007\/978-3-540-39804-2_12","volume":"2838","author":"NV Chawla","year":"2003","unstructured":"Chawla NV, Lazarevic A, Hall LO, Bowyer KW (2003) SMOTEBoost: improving prediction of the minority class in boosting. Lect Notes Comput Sci 2838:107\u2013119","journal-title":"Lect Notes Comput Sci"},{"key":"953_CR14","unstructured":"Cieslak DA, Chawla NV, Striegel A (2006) Combating imbalance in network intrusion datasets.\u00a0In: IEEE international conference on granular computing, IEEE. Atlanta, USA"},{"key":"953_CR15","doi-asserted-by":"crossref","unstructured":"Cohen WW (1995) Fast effective rule induction. In: Twelfth international conference on machine learning. Morgan Kaufmann Publishers Inc. Tahoe City, California,\u00a0pp 115\u2013123","DOI":"10.1016\/B978-1-55860-377-6.50023-2"},{"issue":"1","key":"953_CR16","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1111\/j.2517-6161.1977.tb01600.x","volume":"39","author":"AP Dempster","year":"1977","unstructured":"Dempster AP, Laird NM, Rubin DB (1977) Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc Ser B Methodol 39(1):1\u201322","journal-title":"J R Stat Soc Ser B Methodol"},{"issue":"1","key":"953_CR17","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1016\/0377-2217(95)00246-4","volume":"95","author":"VS Desai","year":"1996","unstructured":"Desai VS, Crook JN, Jr GO (1996) A comparison of neural networks and linear scoring models in the credit union environment. Eur J Oper Res 95(1):24\u201337","journal-title":"Eur J Oper Res"},{"key":"953_CR18","unstructured":"Domingos P (1999) Metacost: a general method for making classifiers cost-sensitive. In:\u00a0KDD\u201999 proceedings of the ifth ACM\u00a0SIGKDD international conference on knowledge discovery and\u00a0data mining. San Diego, USA,\u00a0vol 99, pp 155\u2013164"},{"issue":"8","key":"953_CR19","doi-asserted-by":"publisher","first-page":"861","DOI":"10.1016\/j.patrec.2005.10.010","volume":"27","author":"T Fawcett","year":"2006","unstructured":"Fawcett T (2006) An introduction to ROC analysis. Pattern Recognit Lett 27(8):861\u2013874","journal-title":"Pattern Recognit Lett"},{"issue":"3","key":"953_CR20","doi-asserted-by":"publisher","first-page":"285","DOI":"10.3233\/AIC-2011-0490","volume":"24","author":"A Freitas","year":"2011","unstructured":"Freitas A (2011) Building cost-sensitive decision trees for medical applications. AI Commun 24(3):285\u2013287","journal-title":"AI Commun"},{"issue":"12","key":"953_CR21","doi-asserted-by":"publisher","first-page":"3460","DOI":"10.1016\/j.patcog.2013.05.006","volume":"46","author":"M Galar","year":"2013","unstructured":"Galar M, Barrenechea E, Herrera F (2013) EUSBoost: enhancing ensembles for highly imbalanced data-sets by evolutionary undersampling. Pattern Recognit 46(12):3460\u20133471","journal-title":"Pattern Recognit"},{"issue":"4","key":"953_CR22","doi-asserted-by":"publisher","first-page":"463","DOI":"10.1109\/TSMCC.2011.2161285","volume":"42","author":"M Galar","year":"2012","unstructured":"Galar M, Fernandez A, Barrenechea E, Bustince H, Herrera F (2012) A review on ensembles for the class imbalance problem: bagging-, boosting-, and hybrid-based approaches. IEEE Trans Syst Man Cybern Part C Appl Rev 42(4):463\u2013484","journal-title":"IEEE Trans Syst Man Cybern Part C Appl Rev"},{"issue":"18","key":"953_CR23","doi-asserted-by":"publisher","first-page":"13267","DOI":"10.1016\/j.eswa.2012.05.075","volume":"39","author":"V Garc\u00eda","year":"2012","unstructured":"Garc\u00eda V, Marqu\u00e9s AI, S\u00e1nchez JS (2012) On the use of data filtering techniques for credit risk prediction with instance-based models. Expert Syst Appl 39(18):13267\u201313276","journal-title":"Expert Syst Appl"},{"key":"953_CR24","doi-asserted-by":"publisher","first-page":"535","DOI":"10.1016\/j.neucom.2013.05.003","volume":"122","author":"A Ghazikhani","year":"2013","unstructured":"Ghazikhani A, Monsefi R, Yazdi HS (2013) Ensemble of online neural networks for non-stationary and imbalanced data streams. Neurocomputing 122:535\u2013544","journal-title":"Neurocomputing"},{"key":"953_CR25","first-page":"220","volume":"73","author":"H Guo","year":"2016","unstructured":"Guo H, Li Y, Shang J, Gu M, Huang Y, Gong B (2016) Learning from class-imbalanced data: review of methods and applications. Expert Syst Appl 73:220\u2013239","journal-title":"Expert Syst Appl"},{"key":"953_CR26","doi-asserted-by":"crossref","unstructured":"Han H, Wang WY, Mao BH (2005) Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning. In:\u00a0International conference on intelligent computing.\u00a0Springer, Berlin, Heidelberg.\u00a0Ulsan, Korea,\u00a0pp 878\u2013887","DOI":"10.1007\/11538059_91"},{"issue":"3","key":"953_CR27","doi-asserted-by":"publisher","first-page":"523","DOI":"10.1111\/j.1467-985X.1997.00078.x","volume":"160","author":"DJ Hand","year":"1997","unstructured":"Hand DJ, Henley WE (1997) Statistical classification methods in consumer credit scoring: a review. J R Stat Soc 160(3):523\u2013541","journal-title":"J R Stat Soc"},{"issue":"1","key":"953_CR28","first-page":"100","volume":"28","author":"JA Hartigan","year":"1979","unstructured":"Hartigan JA, Wong MA (1979) Algorithm AS 136: a k-means clustering algorithm. J R Stat Soc 28(1):100\u2013108","journal-title":"J R Stat Soc"},{"key":"953_CR29","unstructured":"Hu S, Liang Y, Ma L, He Y (2009) MSMOTE: improving classification performance when training data is imbalanced. In:\u00a02009 second international workshop on computer science and engineering,\u00a0IEEE. Qingdao, China, vol 2, pp 13\u201317"},{"issue":"4","key":"953_CR30","doi-asserted-by":"publisher","first-page":"543","DOI":"10.1016\/S0167-9236(03)00086-1","volume":"37","author":"Z Huang","year":"2004","unstructured":"Huang Z, Chen H, Hsu CJ, Chen WH, Wu S (2004) Credit rating analysis with support vector machines and neural networks: a market comparative study. Decis Support Syst 37(4):543\u2013558","journal-title":"Decis Support Syst"},{"key":"953_CR31","doi-asserted-by":"crossref","unstructured":"Jackowski K, Krawczyk B, Wo\u017aniak M (2012) Cost-sensitive splitting and selection method for medical decision support system. In: Intelligent data engineering and automated learning\u2014IDEAL 2012. Springer, Berlin","DOI":"10.1007\/978-3-642-32639-4_101"},{"issue":"5","key":"953_CR32","doi-asserted-by":"publisher","first-page":"509","DOI":"10.1016\/j.compbiomed.2010.03.005","volume":"40","author":"DC Li","year":"2010","unstructured":"Li DC, Liu CW, Hu SC (2010) A learning method for the class imbalance problem with medical data sets. Comput Biol Med 40(5):509\u2013518","journal-title":"Comput Biol Med"},{"issue":"5","key":"953_CR33","doi-asserted-by":"publisher","first-page":"429","DOI":"10.3233\/IDA-2002-6504","volume":"6","author":"N Japkowicz","year":"2002","unstructured":"Japkowicz N, Stephen S (2002) The class imbalance problem: a systematic study. Intell Data Anal 6(5):429\u2013449","journal-title":"Intell Data Anal"},{"key":"953_CR34","unstructured":"Kasabov N (2002) Evolving connectionist systems for adaptive learning and knowledge discovery: methods, tools, applications. In:\u00a0Proceedings first international IEEE symposium intelligent systems, IEEE. Varna, Bulgaria, vol 1, pp 24\u201328"},{"issue":"4","key":"953_CR35","doi-asserted-by":"publisher","first-page":"269","DOI":"10.1016\/j.neucom.2013.09.049","volume":"134","author":"N Kasabov","year":"2014","unstructured":"Kasabov N, Feigin V, Hou ZG, Chen Y, Liang L, Krishnamurthi R, Parmar P (2014) Evolving spiking neural networks for personalised modelling, classification and prediction of spatio-temporal patterns with a case study on stroke. Neurocomputing 134(4):269\u2013279","journal-title":"Neurocomputing"},{"issue":"99","key":"953_CR36","first-page":"887","volume":"PP","author":"NK Kasabov","year":"2016","unstructured":"Kasabov NK, Doborjeh MG, Doborjeh ZG (2016) Mapping, learning, visualization, classification, and understanding of fMRI data in the NeuCube evolving spatiotemporal data machine of spiking neural networks. IEEE Trans Neural Netw Learn Syst PP(99):887\u2013899","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"953_CR37","unstructured":"Kohavi R (1995) A study of cross-validation and bootstrap for accuracy estimation and model selection. In:\u00a0The international joint conference on artiicial intelligence, Morgan Kaufmann. Los Angeles, CA, vol 14, no 2, pp 1137\u20131145"},{"issue":"1","key":"953_CR38","first-page":"25","volume":"30","author":"S Kotsiantis","year":"2006","unstructured":"Kotsiantis S, Kanellopoulos D, Pintelas P (2006) Handling imbalanced datasets: a review. GESTS Int Trans Comput Sci Eng 30(1):25\u201336","journal-title":"GESTS Int Trans Comput Sci Eng"},{"issue":"1","key":"953_CR39","doi-asserted-by":"publisher","first-page":"554","DOI":"10.1016\/j.asoc.2013.08.014","volume":"14","author":"B Krawczyk","year":"2014","unstructured":"Krawczyk B, Woniak M, Schaefer G (2014) Cost-sensitive decision tree ensembles for effective imbalanced classification. Appl Soft Comput 14(1):554\u2013562","journal-title":"Appl Soft Comput"},{"key":"953_CR40","unstructured":"Kubat M, Matwin S (1997) Addressing the curse of imbalanced training sets: one-sided selection. In:\u00a0the 14th\u00a0international conference on machine learning. Nashville, TN, USA, vol 97, pp 179\u2013186"},{"key":"953_CR41","doi-asserted-by":"publisher","first-page":"634","DOI":"10.1007\/978-3-540-68125-0_59","volume":"5012","author":"P Lenca","year":"2008","unstructured":"Lenca P, Lallich S (2008) A comparison of different off-centered entropies to deal with class imbalance for decision trees. Lect Notes Comput Sci 5012:634\u2013643","journal-title":"Lect Notes Comput Sci"},{"key":"953_CR42","doi-asserted-by":"crossref","unstructured":"Li Y, Sun G, Zhu Y (2010) Data imbalance problem in text classification. In:\u00a02010 third international symposium on information processing, IEEE. Qingdao, China, pp 301\u2013305","DOI":"10.1109\/ISIP.2010.47"},{"key":"953_CR43","unstructured":"Lin Y, Huang X, Xu K (2013) Research on extreme risk warning for financial market based on RU-SMOTE-SVM.\u00a0Forecasting 32(4)"},{"key":"953_CR44","doi-asserted-by":"publisher","unstructured":"Liu TY (2012) Feature selection based on mutual information for gear imbalanced problem faulty diagnosis. In: IET conference publications, 2012, pp 54\u201354. https:\/\/doi.org\/10.1049\/cp.2012.0506","DOI":"10.1049\/cp.2012.0506"},{"key":"953_CR45","doi-asserted-by":"publisher","unstructured":"Liu W, Chawla S (2011) Class confidence weighted kNN algorithms for imbalanced data sets. In: Computer science. https:\/\/doi.org\/10.1007\/978-3-642-20847-8 , pp 345\u2013356 (chapter 29)","DOI":"10.1007\/978-3-642-20847-8"},{"key":"953_CR46","doi-asserted-by":"crossref","unstructured":"Liu W, Chawla S, Cieslak DA, Chawla NV (2010) A robust decision tree algorithm for imbalanced data sets. In: Paper presented at the SIAM international conference on data mining, SDM 2010, April 29\u2013May 1, 2010, Columbus, Ohio, USA","DOI":"10.1137\/1.9781611972801.67"},{"issue":"2","key":"953_CR47","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2431211.2431215","volume":"45","author":"S Lomax","year":"2013","unstructured":"Lomax S, Vadera S (2013) A survey of cost-sensitive decision tree induction algorithms. ACM Comput Surv 45(2):1\u201335","journal-title":"ACM Comput Surv"},{"issue":"1","key":"953_CR48","doi-asserted-by":"publisher","first-page":"168","DOI":"10.1016\/j.csda.2010.06.014","volume":"55","author":"M Maalouf","year":"2011","unstructured":"Maalouf M, Trafalis TB (2011) Robust weighted kernel logistic regression in imbalanced and rare events data. Comput Stat Data Anal 55(1):168\u2013183","journal-title":"Comput Stat Data Anal"},{"issue":"7","key":"953_CR49","doi-asserted-by":"publisher","first-page":"1060","DOI":"10.1057\/jors.2012.120","volume":"64","author":"AI Marqu\u00e9s","year":"2013","unstructured":"Marqu\u00e9s AI, Garc\u00eda V, S\u00e1nchez JS (2013) On the suitability of resampling techniques for the class imbalance problem in credit scoring. J Oper Res Soc 64(7):1060\u20131070","journal-title":"J Oper Res Soc"},{"key":"953_CR50","unstructured":"Mena L, Gonzalez JA (2006) Machine learning for imbalanced datasets: application in medical diagnostic. In: Paper presented at the nineteenth international Florida artificial intelligence research society conference, Melbourne Beach, Florida, USA, May"},{"key":"953_CR51","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-31900-6_45","volume-title":"A competition strategy to cost-sensitive decision trees","author":"F Min","year":"2012","unstructured":"Min F, Zhu W (2012) A competition strategy to cost-sensitive decision trees. Springer, Berlin"},{"issue":"4","key":"953_CR52","doi-asserted-by":"publisher","first-page":"589","DOI":"10.1111\/j.1540-6261.1968.tb00843.x","volume":"23","author":"EI Altman","year":"1968","unstructured":"Altman EI (1968) Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. J Financ 23(4):589\u2013609","journal-title":"J Financ"},{"issue":"2","key":"953_CR53","first-page":"19","volume":"30","author":"J Perols","year":"2013","unstructured":"Perols J (2013) Financial statement fraud detection: an analysis of statistical and machine learning algorithms. Soc Sci Electron Publ 30(2):19\u201350","journal-title":"Soc Sci Electron Publ"},{"issue":"3","key":"953_CR54","first-page":"79","volume":"6","author":"K Pluto","year":"2005","unstructured":"Pluto K, Tasche D (2005) Estimating probabilities of default for low default portfolios. Dirk Tasche 6(3):79\u2013103","journal-title":"Dirk Tasche"},{"issue":"4","key":"953_CR55","first-page":"3216","volume":"3","author":"S Rodda","year":"2011","unstructured":"Rodda S, Mogalla S (2011) A normalized measure for estimating classification rules for multi-class imbalanced datasets. Int J Eng Sci Technol 3(4):3216\u20133220","journal-title":"Int J Eng Sci Technol"},{"issue":"20","key":"953_CR56","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/0377-0427(87)90125-7","volume":"20","author":"P Rousseeuw","year":"1987","unstructured":"Rousseeuw P (1987) Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J Comput Appl Math 20(20):53\u201365","journal-title":"J Comput Appl Math"},{"issue":"1","key":"953_CR57","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1016\/0167-6687(89)90044-9","volume":"8","author":"A Steenackers","year":"1989","unstructured":"Steenackers A, Goovaerts MJ (1989) A credit scoring model for personal loans. Insur Math Econ 8(1):31\u201334","journal-title":"Insur Math Econ"},{"issue":"12","key":"953_CR58","doi-asserted-by":"publisher","first-page":"3358","DOI":"10.1016\/j.patcog.2007.04.009","volume":"40","author":"Y Sun","year":"2007","unstructured":"Sun Y, Kamel MS, Wong AKC, Wang Y (2007) Cost-sensitive boosting for classification of imbalanced data. Pattern Recognit 40(12):3358\u20133378","journal-title":"Pattern Recognit"},{"issue":"3","key":"953_CR59","doi-asserted-by":"publisher","first-page":"309","DOI":"10.1002\/sec.564","volume":"6","author":"C Thomas","year":"2013","unstructured":"Thomas C (2013) Improving intrusion detection for imbalanced network traffic. Secur Commun Netw 6(3):309\u2013324","journal-title":"Secur Commun Netw"},{"key":"953_CR60","doi-asserted-by":"publisher","DOI":"10.1137\/1.9780898718317","volume-title":"Credit scoring and its applications","author":"LC Thomas","year":"2002","unstructured":"Thomas LC, Crook J, Edelman D (2002) Credit scoring and its applications. SIAM, Philadelphia"},{"issue":"11","key":"953_CR61","first-page":"769","volume":"6","author":"I Tomek","year":"1976","unstructured":"Tomek I (1976) Two modifications of CNN. IEEE Trans Syst Man Cybern SMC 6(11):769\u2013772","journal-title":"IEEE Trans Syst Man Cybern SMC"},{"issue":"1","key":"953_CR62","doi-asserted-by":"publisher","first-page":"223","DOI":"10.1016\/j.eswa.2010.06.048","volume":"38","author":"G Wang","year":"2011","unstructured":"Wang G, Hao J, Ma J, Jiang H (2011) A comparative assessment of ensemble learning for credit scoring. Expert Syst Appl 38(1):223\u2013230","journal-title":"Expert Syst Appl"},{"key":"953_CR63","doi-asserted-by":"crossref","unstructured":"Wang S, Yao X (2009) Diversity analysis on imbalanced data sets by using ensemble models. In:\u00a02009 IEEE symposium on computational intelligence and data mining, IEEE. Nashville, TN, USA, pp 324\u2013331","DOI":"10.1109\/CIDM.2009.4938667"},{"issue":"11","key":"953_CR64","doi-asserted-by":"publisher","first-page":"1131","DOI":"10.1016\/S0305-0548(99)00149-5","volume":"27","author":"D West","year":"2000","unstructured":"West D (2000) Neural network credit scoring models. Comput Oper Res 27(11):1131\u20131152","journal-title":"Comput Oper Res"},{"issue":"3","key":"953_CR65","doi-asserted-by":"publisher","first-page":"757","DOI":"10.2307\/2330408","volume":"15","author":"JC Wiginton","year":"1980","unstructured":"Wiginton JC (1980) A note on the comparison of logit and discriminant models of consumer credit behavior. J Financ Quant Anal 15(3):757\u2013770","journal-title":"J Financ Quant Anal"},{"issue":"3","key":"953_CR66","doi-asserted-by":"publisher","first-page":"1521","DOI":"10.1016\/j.ejor.2006.10.066","volume":"183","author":"Y Yang","year":"2007","unstructured":"Yang Y (2007) Adaptive credit scoring with kernel learning methods. Eur J Oper Res 183(3):1521\u20131536","journal-title":"Eur J Oper Res"},{"issue":"2","key":"953_CR67","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1093\/imaman\/11.2.111","volume":"11","author":"MB Yobas","year":"2000","unstructured":"Yobas MB, Crook JN, Ross P (2000) Credit scoring using neural and evolutionary techniques. IMA J Manag Math 11(2):111\u2013125","journal-title":"IMA J Manag Math"},{"issue":"1","key":"953_CR68","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1145\/1007730.1007741","volume":"6","author":"Z Zheng","year":"2004","unstructured":"Zheng Z, Wu X, Srihari R (2004) Feature selection for text categorization on imbalanced data. Sigkdd Explor 6(1):80\u201389","journal-title":"Sigkdd Explor"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-019-00953-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s13042-019-00953-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-019-00953-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,17]],"date-time":"2024-07-17T19:06:09Z","timestamp":1721243169000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s13042-019-00953-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,5,8]]},"references-count":68,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2019,12]]}},"alternative-id":["953"],"URL":"https:\/\/doi.org\/10.1007\/s13042-019-00953-2","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,5,8]]},"assertion":[{"value":"19 April 2018","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 April 2019","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 May 2019","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}