{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T04:48:03Z","timestamp":1770698883276,"version":"3.49.0"},"reference-count":64,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2018,9,5]],"date-time":"2018-09-05T00:00:00Z","timestamp":1536105600000},"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":["71671019"],"award-info":[{"award-number":["71671019"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["71701116"],"award-info":[{"award-number":["71701116"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"MOE (Ministry of Education in China) Project of Humanities and Social Sciences","award":["15YJC630016"],"award-info":[{"award-number":["15YJC630016"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2019,2]]},"DOI":"10.1007\/s10489-018-1253-8","type":"journal-article","created":{"date-parts":[[2018,9,5]],"date-time":"2018-09-05T01:22:02Z","timestamp":1536110522000},"page":"555-568","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["Dynamic weighted ensemble classification for credit scoring using Markov Chain"],"prefix":"10.1007","volume":"49","author":[{"given":"Xiaodong","family":"Feng","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2897-888X","authenticated-orcid":false,"given":"Zhi","family":"Xiao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bo","family":"Zhong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuanxiang","family":"Dong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing","family":"Qiu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2018,9,5]]},"reference":[{"issue":"4","key":"1253_CR1","doi-asserted-by":"publisher","first-page":"421","DOI":"10.1109\/TSMCC.2011.2170420","volume":"42","author":"WY Lin","year":"2012","unstructured":"Lin WY, Hu YH, Tsai CF (2012) Machine learning in financial crisis prediction: a survey. IEEE T Syst Man Cy C 42(4):421\u2013436. \n                    https:\/\/doi.org\/10.1109\/tsmcc.2011.2170420","journal-title":"IEEE T Syst Man Cy C"},{"issue":"1","key":"1253_CR2","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1007\/s10489-009-0177-8","volume":"34","author":"A Bahrammirzaee","year":"2011","unstructured":"Bahrammirzaee A, Ghatari AR, Ahmadi P, Madani K (2011) Hybrid credit ranking intelligent system using expert system and artificial neural networks. Appl Intell 34(1):28\u201346. \n                    https:\/\/doi.org\/10.1007\/s10489-009-0177-8","journal-title":"Appl Intell"},{"key":"1253_CR3","volume-title":"Basel III: a global regulatory framework for more resilient banks and banking systems","author":"BCBS","year":"2011","unstructured":"BCBS (2011) Basel III: a global regulatory framework for more resilient banks and banking systems. Bank for International Settlements, Basel"},{"issue":"1","key":"1253_CR4","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1016\/j.ejor.2015.05.030","volume":"247","author":"S Lessmann","year":"2015","unstructured":"Lessmann S, Baesens B, Seow HV, Thomas LC (2015) Benchmarking state-of-the-art classification algorithms for credit scoring: an update of research. Eur J Oper Res 247(1):124\u2013136","journal-title":"Eur J Oper Res"},{"issue":"4","key":"1253_CR5","doi-asserted-by":"publisher","first-page":"835","DOI":"10.1016\/S0378-4266(03)00202-4","volume":"28","author":"RB Avery","year":"2004","unstructured":"Avery RB, Calem PS, Canner GB (2004) Consumer credit scoring: do situational circumstances matter? J Banking Finance 28(4):835\u2013856. \n                    https:\/\/doi.org\/10.1016\/j.jbankfin.2003.10.009","journal-title":"J Banking Finance"},{"key":"1253_CR6","unstructured":"Zhou ZH (2008) Knowledge acquisition via ensemble learning. In: 2008 international forum on knowledge technology, pp 361\u2013362"},{"key":"1253_CR7","volume-title":"Ensemble learning","author":"R Polikar","year":"2012","unstructured":"Polikar R (2012) Ensemble learning. Springer, US"},{"key":"1253_CR8","unstructured":"Zhang CX, Duin RPT (2009) An empirical study of a linear regression combiner on multi-class data sets. In: Benediktsson, JA, Kittler, J, Roli, F (edn). Multiple classifier systems, proceedings, vol 5519. Lecture Notes in Computer Science, pp 478\u2013487"},{"key":"1253_CR9","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/j.knosys.2017.03.026","volume":"125","author":"ZL Zhang","year":"2017","unstructured":"Zhang ZL, Luo XG, Garcia S, Tang JF, Herrera F (2017) Exploring the effectiveness of dynamic ensemble selection in the one-versus-one scheme. Knowl-Based Syst 125:53\u201363","journal-title":"Knowl-Based Syst"},{"issue":"3","key":"1253_CR10","doi-asserted-by":"publisher","first-page":"309","DOI":"10.1007\/s00521-010-0372-x","volume":"20","author":"YQ Zhu","year":"2011","unstructured":"Zhu Y Q, Ou J S, Chen G, Yu H P (2011) Dynamic weighting ensemble classifiers based on cross-validation. Neural Comput Appl 20(3):309\u2013317","journal-title":"Neural Comput Appl"},{"issue":"3","key":"1253_CR11","doi-asserted-by":"publisher","first-page":"1447","DOI":"10.1016\/j.ejor.2006.09.100","volume":"183","author":"JN Crook","year":"2007","unstructured":"Crook J N, Edelman D B, Thomas L C (2007) Recent developments in consumer credit risk assessment. Eur J Oper Res 183(3):1447\u20131465. \n                    https:\/\/doi.org\/10.1016\/j.ejor.2006.09.100","journal-title":"Eur J Oper Res"},{"key":"1253_CR12","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1016\/j.knosys.2017.07.034","volume":"134","author":"A Beque","year":"2017","unstructured":"Beque A, Coussement K, Gayler R, Lessmann S (2017) Approaches for credit scorecard calibration: an empirical analysis. Knowl-Based Syst 134:213\u2013227. \n                    https:\/\/doi.org\/10.1016\/j.knosys.2017.07.034","journal-title":"Knowl-Based Syst"},{"issue":"2","key":"1253_CR13","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1023\/A:1007607513941","volume":"40","author":"TG Dietterich","year":"2000","unstructured":"Dietterich T G (2000) An experimental comparison of three methods for constructing ensembles of decision trees: bagging, boosting, and randomization. Mach Learn 40(2):139\u2013157. \n                    https:\/\/doi.org\/10.1023\/a:1007607513941","journal-title":"Mach Learn"},{"issue":"2","key":"1253_CR14","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1111\/j.1469-1809.1936.tb02137.x","volume":"7","author":"RA Fisher","year":"1936","unstructured":"Fisher R A (1936) The use of multiple measurements in taxonomic problems. Ann Eugen 7(2):179\u2013188","journal-title":"Ann Eugen"},{"key":"1253_CR15","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 D J, Henley W E (1997) Statistical classification methods in consumer credit scoring: a review. J Royal Stat Soc Ser A (Statistics in Society) 160:523\u2013541","journal-title":"J Royal Stat Soc Ser A (Statistics in Society)"},{"issue":"9","key":"1253_CR16","doi-asserted-by":"publisher","first-page":"1384","DOI":"10.1057\/jors.2012.145","volume":"64","author":"A Marques","year":"2012","unstructured":"Marques A, Garc\u00eda V, Sanchez J (2012) A literature review on the application of evolutionary computing to credit scoring. J Oper Res Soc 64(9):1384\u20131399","journal-title":"J Oper Res Soc"},{"issue":"2","key":"1253_CR17","doi-asserted-by":"publisher","first-page":"374","DOI":"10.1016\/j.asoc.2009.08.003","volume":"10","author":"C-F Tsai","year":"2010","unstructured":"Tsai C -F, Chen M -L (2010) Credit rating by hybrid machine learning techniques. Appl Soft Comput 10 (2):374\u2013380","journal-title":"Appl Soft Comput"},{"issue":"3","key":"1253_CR18","doi-asserted-by":"publisher","first-page":"271","DOI":"10.1002\/for.1124","volume":"29","author":"B Qian","year":"2010","unstructured":"Qian B, Rasheed K (2010) Foreign exchange market prediction with multiple classifiers. J Forecasting 29 (3):271\u2013284. \n                    https:\/\/doi.org\/10.1002\/for.1124","journal-title":"J Forecasting"},{"issue":"8","key":"1253_CR19","doi-asserted-by":"publisher","first-page":"2254","DOI":"10.1016\/j.asoc.2012.03.028","volume":"12","author":"J Sun","year":"2012","unstructured":"Sun J, Li H (2012) Financial distress prediction using support vector machines: ensemble vs. individual. Appl Soft Comput 12(8):2254\u20132265","journal-title":"Appl Soft Comput"},{"issue":"1","key":"1253_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10462-015-9434-x","volume":"45","author":"N Chen","year":"2016","unstructured":"Chen N, Ribeiro B, Chen A (2016) Financial credit risk assessment: a recent review. Artif Intell Rev 45(1):1\u201323","journal-title":"Artif Intell Rev"},{"issue":"2","key":"1253_CR21","doi-asserted-by":"publisher","first-page":"180","DOI":"10.1002\/for.1265","volume":"32","author":"H Li","year":"2013","unstructured":"Li H, Sun J (2013) Predicting business failure using an RSF-based case-based reasoning ensemble forecasting method. J Forecasting 32(2):180\u2013192","journal-title":"J Forecasting"},{"issue":"2","key":"1253_CR22","doi-asserted-by":"publisher","first-page":"122","DOI":"10.1002\/for.2418","volume":"36","author":"LA Yu","year":"2017","unstructured":"Yu L A, Zhao Y, Tang L (2017) Ensemble forecasting for complex time series using sparse representation and neural networks. J Forecasting 36(2):122\u2013138","journal-title":"J Forecasting"},{"key":"1253_CR23","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1016\/j.knosys.2015.04.017","volume":"85","author":"LG Zhou","year":"2015","unstructured":"Zhou L G, Lu D, Fujita H (2015) The performance of corporate financial distress prediction models with features selection guided by domain knowledge and data mining approaches. Knowl-Based Syst 85:52\u201361. \n                    https:\/\/doi.org\/10.1016\/j.knosys.2015.04.017","journal-title":"Knowl-Based Syst"},{"key":"1253_CR24","unstructured":"Zhang C X, Duin R P W (2009) An empirical study of a linear regression combiner on multi-class data sets. In: Proceedings of multiple classifier systems, international workshop, MCS, vol 2009. Reykjavik, Iceland, pp 478\u2013487"},{"issue":"8","key":"1253_CR25","doi-asserted-by":"publisher","first-page":"3825","DOI":"10.1016\/j.eswa.2013.12.003","volume":"41","author":"J Abell\u00e1n","year":"2014","unstructured":"Abell\u00e1n J, Mantas C J (2014) Improving experimental studies about ensembles of classifiers for bankruptcy prediction and credit scoring. Expert Syst Appl 41(8):3825\u20133830","journal-title":"Expert Syst Appl"},{"key":"1253_CR26","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1016\/j.knosys.2016.04.013","volume":"104","author":"M Ala\u2019raj","year":"2016","unstructured":"Ala\u2019raj M, Abbod M F (2016) Classifiers consensus system approach for credit scoring. Knowl-Based Syst 104:89\u2013105. \n                    https:\/\/doi.org\/10.1016\/j.knosys.2016.04.013","journal-title":"Knowl-Based Syst"},{"issue":"2","key":"1253_CR27","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1016\/S0167-9236(02)00079-9","volume":"34","author":"E Kim","year":"2003","unstructured":"Kim E, Kim W, Lee Y (2003) Combination of multiple classifiers for the customer\u2019s purchase behavior prediction. Decis Support Syst 34(2):167\u2013175","journal-title":"Decis Support Syst"},{"issue":"14","key":"1253_CR28","doi-asserted-by":"publisher","first-page":"1756","DOI":"10.1016\/j.patrec.2011.07.009","volume":"32","author":"CX Zhang","year":"2011","unstructured":"Zhang C X, Duin R P W (2011) An experimental study of one- and two-level classifier fusion for different sample sizes. Pattern Recogn Lett 32(14):1756\u20131767","journal-title":"Pattern Recogn Lett"},{"key":"1253_CR29","unstructured":"Duin RPW, Tax DMJ (1998) Classifier conditional posterior probabilities. In: Joint Iapr international workshops on advances in pattern recognition, pp 611\u2013619"},{"key":"1253_CR30","doi-asserted-by":"publisher","first-page":"271","DOI":"10.1613\/jair.594","volume":"10","author":"KM Ting","year":"1999","unstructured":"Ting K M, Witten I H (1999) Issues in stacked generalization. J Artif Intell Res 10:271\u2013289","journal-title":"J Artif Intell Res"},{"key":"1253_CR31","doi-asserted-by":"crossref","unstructured":"Kuncheva LI (2014) Combining pattern classifiers: methods and algorithms, 2nd edn","DOI":"10.1002\/9781118914564"},{"issue":"2","key":"1253_CR32","doi-asserted-by":"publisher","first-page":"1351","DOI":"10.1016\/j.eswa.2009.06.083","volume":"37","author":"LA Yu","year":"2010","unstructured":"Yu L A, Yue W Y, Wang S Y, Lai K K (2010) Support vector machine based multiagent ensemble learning for credit risk evaluation. Expert Syst Appl 37(2):1351\u20131360","journal-title":"Expert Syst Appl"},{"issue":"5","key":"1253_CR33","doi-asserted-by":"publisher","first-page":"551","DOI":"10.1017\/S0269888913000155","volume":"29","author":"A Jurek","year":"2014","unstructured":"Jurek A, Bi Y X, Wu S L, Nugent C (2014) A survey of commonly used ensemble-based classification techniques. Knowl Eng Rev 29(5):551\u2013581","journal-title":"Knowl Eng Rev"},{"key":"1253_CR34","doi-asserted-by":"publisher","first-page":"695","DOI":"10.1016\/j.procs.2013.05.090","volume":"17","author":"L Zhang","year":"2013","unstructured":"Zhang L, Zhang L L, Teng W L, Chen Y B (2013) Based on information fusion technique with data mining in the application of finance early-warning. Procedia Comput Sci 17:695\u2013703. \n                    https:\/\/doi.org\/10.1016\/j.procs.2013.05.090","journal-title":"Procedia Comput Sci"},{"issue":"5","key":"1253_CR35","doi-asserted-by":"publisher","first-page":"1718","DOI":"10.1016\/j.patcog.2007.10.015","volume":"41","author":"AHR Ko","year":"2008","unstructured":"Ko A H R, Sabourin R, Britto A S (2008) From dynamic classifier selection to dynamic ensemble selection. Pattern Recogn 41(5):1718\u20131731","journal-title":"Pattern Recogn"},{"issue":"3","key":"1253_CR36","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1016\/j.inffus.2011.03.007","volume":"13","author":"T Woloszynski","year":"2012","unstructured":"Woloszynski T, Kurzynski M, Podsiadlo P, Stachowiak G W (2012) A measure of competence based on random classification for dynamic ensemble selection. Inf Fusion 13(3):207\u2013213. \n                    https:\/\/doi.org\/10.1016\/j.inffus.2011.03.007","journal-title":"Inf Fusion"},{"issue":"10\u201311","key":"1253_CR37","doi-asserted-by":"publisher","first-page":"2656","DOI":"10.1016\/j.patcog.2011.03.020","volume":"44","author":"T Woloszynski","year":"2011","unstructured":"Woloszynski T, Kurzynski M (2011) A probabilistic model of classifier competence for dynamic ensemble selection. Pattern Recogn 44(10\u201311):2656\u20132668. \n                    https:\/\/doi.org\/10.1016\/j.patcog.2011.03.020","journal-title":"Pattern Recogn"},{"issue":"10","key":"1253_CR38","doi-asserted-by":"publisher","first-page":"2993","DOI":"10.1016\/j.patcog.2008.03.027","volume":"41","author":"EM Dos Santos","year":"2008","unstructured":"Dos Santos E M, Sabourin R, Maupin P (2008) A dynamic overproduce-and-choose strategy for the selection of classifier ensembles. Pattern Recogn 41(10):2993\u20133009. \n                    https:\/\/doi.org\/10.1016\/j.patcog.2008.03.027","journal-title":"Pattern Recogn"},{"key":"1253_CR39","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1016\/j.knosys.2016.12.019","volume":"120","author":"J Sun","year":"2017","unstructured":"Sun J, Fujita H, Chen P, Li H (2017) Dynamic financial distress prediction with concept drift based on time weighting combined with adaboost support vector machine ensemble. Knowl-Based Syst 120:4\u201314. \n                    https:\/\/doi.org\/10.1016\/j.knosys.2016.12.019","journal-title":"Knowl-Based Syst"},{"issue":"5","key":"1253_CR40","first-page":"533","volume":"3","author":"E Cinlar","year":"2015","unstructured":"Cinlar E (2015) Introduction to stochastic process. IEEE Trans Syst Man Cybern SMC 3(5):533\u2013533","journal-title":"IEEE Trans Syst Man Cybern SMC"},{"issue":"2","key":"1253_CR41","doi-asserted-by":"publisher","first-page":"481","DOI":"10.1093\/rfs\/10.2.481","volume":"10","author":"RA Jarrow","year":"1997","unstructured":"Jarrow R A, Lando D, Turnbull S M (1997) A Markov model for the term structure of credit risk spreads. Rev Financ Stud 10(2):481\u2013523","journal-title":"Rev Financ Stud"},{"issue":"4","key":"1253_CR42","doi-asserted-by":"publisher","first-page":"637","DOI":"10.1134\/S0005117912040042","volume":"73","author":"GAF Timofeeva","year":"2012","unstructured":"Timofeeva G A F, Timofeev N (2012) Forecasting credit portfolio components with a Markov chain model. Autom Remote Control 73(4):637\u2013651","journal-title":"Autom Remote Control"},{"key":"1253_CR43","doi-asserted-by":"crossref","unstructured":"Liu K, Lai KK, Guu S-M (2009) Dynamic credit scoring on consumer behavior using fuzzy Markov model. In: Fourth international multi-conference on computing in the global information technology, 2009. ICCGI\u201909. IEEE, pp 235\u2013239","DOI":"10.1109\/ICCGI.2009.42"},{"issue":"2","key":"1253_CR44","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1007\/s10614-011-9258-y","volume":"39","author":"ES Fung","year":"2012","unstructured":"Fung E S, Siu T K (2012) A flexible Markov chain approach for multivariate credit ratings. Comput Econ 39(2):135\u2013143","journal-title":"Comput Econ"},{"issue":"3","key":"1253_CR45","doi-asserted-by":"publisher","first-page":"683","DOI":"10.1016\/j.eswa.2006.06.007","volume":"33","author":"Y-K Chen","year":"2007","unstructured":"Chen Y -K (2007) Economic design of variable sampling interval T 2 control charts\u2014a hybrid Markov chain approach with genetic algorithms. Expert Syst Appl 33(3):683\u2013689","journal-title":"Expert Syst Appl"},{"issue":"1","key":"1253_CR46","doi-asserted-by":"publisher","first-page":"59","DOI":"10.21314\/JRMV.2016.147","volume":"10","author":"MR Sousa","year":"2016","unstructured":"Sousa M R, Gama J, Brandao E (2016) Dynamic credit score modeling with short-term and long-term memories: the case of Freddie Mac\u2019s database. J Risk Model Validat 10(1):59\u201380","journal-title":"J Risk Model Validat"},{"issue":"1","key":"1253_CR47","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1016\/j.ejor.2011.01.023","volume":"212","author":"MMC So","year":"2011","unstructured":"So M M C, Thomas L C (2011) Modelling the profitability of credit cards by Markov decision processes. Eur J Oper Res 212(1):123\u2013130","journal-title":"Eur J Oper Res"},{"key":"1253_CR48","doi-asserted-by":"publisher","DOI":"10.1093\/oxfordhb\/9780199546787.013.0010","volume-title":"Markov Chain models of portfolio credit risk","author":"A Lipton","year":"2012","unstructured":"Lipton A, Rennie A, Bielelcki T R, Cr\u00e9pey S, Herbertsson A (2012) Markov Chain models of portfolio credit risk. The Oxford Handbook of Credit Derivatives, Oxford. \n                    https:\/\/doi.org\/10.1093\/oxfordhb\/9780199546787.013.0010"},{"issue":"3","key":"1253_CR49","doi-asserted-by":"publisher","first-page":"1275","DOI":"10.1016\/j.eswa.2007.08.030","volume":"35","author":"H Abdou","year":"2008","unstructured":"Abdou H, Pointon J, El-Masry A (2008) Neural nets versus conventional techniques in credit scoring in Egyptian banking. Expert Syst Appl 35(3):1275\u20131292. \n                    https:\/\/doi.org\/10.1016\/j.eswa.2007.08.030","journal-title":"Expert Syst Appl"},{"issue":"6","key":"1253_CR50","first-page":"988","volume":"8","author":"VN Vapnik","year":"1995","unstructured":"Vapnik V N (1995) The nature of statistical learning theory. IEEE Trans Neural Netw 8(6):988\u2013999","journal-title":"IEEE Trans Neural Netw"},{"key":"1253_CR51","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1016\/j.knosys.2017.05.003","volume":"128","author":"LG Zhou","year":"2017","unstructured":"Zhou L G, Si Y W, Fujita H (2017) Predicting the listing statuses of Chinese-listed companies using decision trees combined with an improved filter feature selection method. Knowl-Based Syst 128:93\u2013101. \n                    https:\/\/doi.org\/10.1016\/j.knosys.2017.05.003","journal-title":"Knowl-Based Syst"},{"key":"1253_CR52","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1016\/j.knosys.2014.03.007","volume":"63","author":"W Xu","year":"2014","unstructured":"Xu W, Xiao Z, Dang X, Yang D L, Yang X L (2014) Financial ratio selection for business failure prediction using soft set theory. Knowl-Based Syst 63:59\u201367. \n                    https:\/\/doi.org\/10.1016\/j.knosys.2014.03.007","journal-title":"Knowl-Based Syst"},{"issue":"3","key":"1253_CR53","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1520\/JTE20130297","volume":"43","author":"W Xu","year":"2015","unstructured":"Xu W, Xiao Z, Yang D L, Yang X L (2015) A novel nonlinear integrated forecasting model of logistic regression and support vector machine for business failure prediction with all sample sizes. J Test Eval 43(3):13. \n                    https:\/\/doi.org\/10.1520\/jte20130297","journal-title":"J Test Eval"},{"key":"1253_CR54","unstructured":"UCI Machine Learning Repository (2013) University of California, School of Information and Computer Science. \n                    http:\/\/archive.ics.uci.edu\/ml"},{"key":"1253_CR55","doi-asserted-by":"crossref","unstructured":"Thomas L C, Crook J, Edelman D (2002), Credit scoring and its applications. SIAM","DOI":"10.1137\/1.9780898718317"},{"issue":"2","key":"1253_CR56","doi-asserted-by":"publisher","first-page":"2473","DOI":"10.1016\/j.eswa.2007.12.020","volume":"36","author":"IC Yeh","year":"2009","unstructured":"Yeh I C, Lien C H (2009) The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients. Expert Syst Appl 36(2):2473\u20132480","journal-title":"Expert Syst Appl"},{"key":"1253_CR57","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1016\/j.asoc.2016.02.022","volume":"43","author":"HS Xiao","year":"2016","unstructured":"Xiao H S, Xiao Z, Wang Y (2016) Ensemble classification based on supervised clustering for credit scoring. Appl Soft Comput 43:73\u201386. \n                    https:\/\/doi.org\/10.1016\/j.asoc.2016.02.022","journal-title":"Appl Soft Comput"},{"issue":"3","key":"1253_CR58","doi-asserted-by":"publisher","first-page":"230","DOI":"10.1002\/for.2335","volume":"34","author":"R Calabrese","year":"2015","unstructured":"Calabrese R, Osmetti S A (2015) Improving forecast of binary rare events data: a GAM-based approach. J Forecasting 34(3):230\u2013 239","journal-title":"J Forecasting"},{"issue":"1","key":"1253_CR59","doi-asserted-by":"publisher","first-page":"168","DOI":"10.1016\/j.ejor.2012.04.009","volume":"222","author":"S Akkoc","year":"2012","unstructured":"Akkoc S (2012) An empirical comparison of conventional techniques, neural networks and the three stage hybrid adaptive neuro fuzzy inference system (ANFIS) model for credit scoring analysis: the case of Turkish credit card data. Eur J Oper Res 222(1):168\u2013178. \n                    https:\/\/doi.org\/10.1016\/j.ejor.2012.04.009","journal-title":"Eur J Oper Res"},{"issue":"3","key":"1253_CR60","doi-asserted-by":"publisher","first-page":"731","DOI":"10.1007\/s10115-012-0572-z","volume":"36","author":"G-E Teng","year":"2013","unstructured":"Teng G -E, He C -Z, Xiao J, Jiang X -Y (2013) Customer credit scoring based on HMM\/GMDH hybrid model. Knowl Inf Syst 36(3):731\u2013747","journal-title":"Knowl Inf Syst"},{"issue":"1","key":"1253_CR61","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1007\/s10994-009-5119-5","volume":"77","author":"DJ Hand","year":"2009","unstructured":"Hand D J (2009) Measuring classifier performance: a coherent alternative to the area under the ROC curve. Mach Learn 77(1):103\u2013123","journal-title":"Mach Learn"},{"issue":"5","key":"1253_CR62","doi-asserted-by":"publisher","first-page":"492","DOI":"10.1016\/j.patrec.2012.12.004","volume":"34","author":"DJ Hand","year":"2013","unstructured":"Hand D J, Anagnostopoulos C (2013) When is the area under the receiver operating characteristic curve an appropriate measure of classifier performance? Pattern Recogn Lett 34(5):492\u2013495","journal-title":"Pattern Recogn Lett"},{"issue":"1","key":"1253_CR63","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1007\/s10844-014-0333-4","volume":"44","author":"V Garcia","year":"2015","unstructured":"Garcia V, Marques A I, Sanchez J S (2015) An insight into the experimental design for credit risk and corporate bankruptcy prediction systems. J Intell Inf Syst 44(1):159\u2013189","journal-title":"J Intell Inf Syst"},{"key":"1253_CR64","first-page":"1","volume":"7","author":"J Dem\u0161ar","year":"2006","unstructured":"Dem\u0161ar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7:1\u201330","journal-title":"J Mach Learn Res"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10489-018-1253-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-018-1253-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-018-1253-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,10,21]],"date-time":"2019-10-21T14:42:27Z","timestamp":1571668947000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10489-018-1253-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,9,5]]},"references-count":64,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2019,2]]}},"alternative-id":["1253"],"URL":"https:\/\/doi.org\/10.1007\/s10489-018-1253-8","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,9,5]]},"assertion":[{"value":"5 September 2018","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}