{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T01:07:11Z","timestamp":1773364031308,"version":"3.50.1"},"reference-count":72,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2022,4,27]],"date-time":"2022-04-27T00:00:00Z","timestamp":1651017600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,4,27]],"date-time":"2022-04-27T00:00:00Z","timestamp":1651017600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/100014718","name":"Innovative Research Group Project of the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No. 71802199"],"award-info":[{"award-number":["No. 71802199"]}],"id":[{"id":"10.13039\/100014718","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Philosophy and Social Science Foundation of Guangdong Province, China","award":["No. GD21TW07-01"],"award-info":[{"award-number":["No. GD21TW07-01"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. & Cyber."],"published-print":{"date-parts":[[2022,7]]},"DOI":"10.1007\/s13042-022-01566-y","type":"journal-article","created":{"date-parts":[[2022,4,27]],"date-time":"2022-04-27T12:04:43Z","timestamp":1651061083000},"page":"2089-2103","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["A novel method for financial distress prediction based on sparse neural networks with $$L_{1\/2}$$ regularization"],"prefix":"10.1007","volume":"13","author":[{"given":"Ying","family":"Chen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8520-3178","authenticated-orcid":false,"given":"Jifeng","family":"Guo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junqin","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bin","family":"Lin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,4,27]]},"reference":[{"issue":"9","key":"1566_CR1","doi-asserted-by":"publisher","first-page":"2283","DOI":"10.1007\/s13042-018-0867-9","volume":"10","author":"M Alfaro-Ponce","year":"2019","unstructured":"Alfaro-Ponce M, Arg\u00fcelles A, Chairez I, P\u00e9rez A (2019) Automatic electroencephalographic information classifier based on recurrent neural networks. Int J Mach Learn Cybern 10(9):2283\u20132295","journal-title":"Int J Mach Learn Cybern"},{"issue":"4","key":"1566_CR2","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":"5","key":"1566_CR3","doi-asserted-by":"publisher","first-page":"1443","DOI":"10.1111\/0022-1082.00062","volume":"53","author":"G Andrade","year":"1998","unstructured":"Andrade G, Kaplan SN (1998) How costly is financial (not economic) distress? Evidence from highly leveraged transactions that became distressed. J Financ 53(5):1443\u20131493","journal-title":"J Financ"},{"key":"1566_CR4","doi-asserted-by":"crossref","unstructured":"BKD, HSW (1991) Evaluating financial distress resolution using prior audit opinions. Contemp Account Res 8(1):97\u2013114","DOI":"10.1111\/j.1911-3846.1991.tb00837.x"},{"issue":"9","key":"1566_CR5","doi-asserted-by":"publisher","first-page":"4061","DOI":"10.1093\/rfs\/hhz110","volume":"33","author":"T Babina","year":"2020","unstructured":"Babina T (2020) Destructive creation at work: how financial distress spurs entrepreneurship. Rev Financ Stud 33(9):4061\u20134101","journal-title":"Rev Financ Stud"},{"issue":"1","key":"1566_CR6","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1016\/j.bar.2005.09.001","volume":"38","author":"S Balcaen","year":"2006","unstructured":"Balcaen S, Ooghe H (2006) 35 years of studies on business failure: an overview of the classic statistical methodologies and their related problems. Br Account Rev 38(1):63\u201393","journal-title":"Br Account Rev"},{"issue":"1","key":"1566_CR7","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1007\/s11142-004-6341-9","volume":"10","author":"WH Beaver","year":"2005","unstructured":"Beaver WH, McNichols MF, Rhie JW (2005) Have financial statements become less informative? evidence from the ability of financial ratios to predict bankruptcy. Rev Acc Stud 10(1):93\u2013122","journal-title":"Rev Acc Stud"},{"key":"1566_CR8","doi-asserted-by":"publisher","first-page":"244","DOI":"10.1016\/j.chieco.2014.07.007","volume":"30","author":"A Bhattacharjee","year":"2014","unstructured":"Bhattacharjee A, Han J (2014) Financial distress of Chinese firms: microeconomic, macroeconomic and institutional influences. China Econ Rev 30:244\u2013262","journal-title":"China Econ Rev"},{"key":"1566_CR9","doi-asserted-by":"crossref","unstructured":"Blum M (1974) Failing company discriminant analysis. J Account Res 1\u201325","DOI":"10.2307\/2490525"},{"issue":"6","key":"1566_CR10","doi-asserted-by":"publisher","first-page":"2899","DOI":"10.1111\/j.1540-6261.2008.01416.x","volume":"63","author":"JY Campbell","year":"2008","unstructured":"Campbell JY, Hilscher J, Szilagyi J (2008) In search of distress risk. J Financ 63(6):2899\u20132939","journal-title":"J Financ"},{"key":"1566_CR11","unstructured":"Casey CJ, McGee VE, Stickney CP (1986) Discriminating between reorganized and liquidated firms in bankruptcy. Account Rev 249\u2013262"},{"issue":"2","key":"1566_CR12","doi-asserted-by":"publisher","first-page":"4075","DOI":"10.1016\/j.eswa.2008.03.020","volume":"36","author":"WS Chen","year":"2009","unstructured":"Chen WS, Du YK (2009) Using neural networks and data mining techniques for the financial distress prediction model. Expert Syst Appl 36(2):4075\u20134086","journal-title":"Expert Syst Appl"},{"issue":"4","key":"1566_CR13","doi-asserted-by":"publisher","first-page":"899","DOI":"10.1007\/s13042-020-01078-7","volume":"11","author":"H Chen","year":"2020","unstructured":"Chen H, Yao M, Gu Q (2020) Pothole detection using location-aware convolutional neural networks. Int J Mach Learn Cybern 11(4):899\u2013911","journal-title":"Int J Mach Learn Cybern"},{"key":"1566_CR14","doi-asserted-by":"crossref","unstructured":"Chen M, Mi D, He P, Deng L, Wei B (2014) A CT reconstruction algorithm based on L1\/2 regularization. Comput Math Methods Med 2014","DOI":"10.1155\/2014\/862910"},{"issue":"3","key":"1566_CR15","first-page":"55","volume":"2","author":"X Chen","year":"2000","unstructured":"Chen X, CZ (2000) Predicting financial distress in Chinese listed firms. China Account Financ Rev 2(3):55\u201392 (in Chinese)","journal-title":"China Account Financ Rev"},{"key":"1566_CR16","first-page":"55","volume":"12","author":"X-G Cui","year":"2007","unstructured":"Cui X-G, Wang L-Y (2007) Over-speed growth, financial crisis and risk forecasting. Account Res 12:55\u201362","journal-title":"Account Res"},{"issue":"4","key":"1566_CR17","doi-asserted-by":"publisher","first-page":"1017","DOI":"10.1111\/j.1540-6261.1980.tb03517.x","volume":"35","author":"IG Dambolena","year":"1980","unstructured":"Dambolena IG, Khoury SJ (1980) Ratio stability and corporate failure. J Financ 35(4):1017\u20131026","journal-title":"J Financ"},{"key":"1566_CR18","doi-asserted-by":"crossref","unstructured":"Davis J, Goadrich M (2006) The relationship between precision-recall and roc curves. In: Proceedings of the 23rd international conference on machine learning, pp 233\u2013240","DOI":"10.1145\/1143844.1143874"},{"key":"1566_CR19","doi-asserted-by":"crossref","unstructured":"Deakin EB (1972) A discriminant analysis of predictors of business failure. J Account Res 167\u2013179","DOI":"10.2307\/2490225"},{"issue":"4","key":"1566_CR20","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1002\/j.1099-1174.1994.tb00068.x","volume":"3","author":"KM Fanning","year":"1994","unstructured":"Fanning KM, Cogger KO (1994) A comparative analysis of artificial neural networks using financial distress prediction. Intell Syst Account Financ Manag 3(4):241\u2013252","journal-title":"Intell Syst Account Financ Manag"},{"issue":"10","key":"1566_CR21","doi-asserted-by":"publisher","first-page":"2901","DOI":"10.1007\/s13042-018-00913-2","volume":"10","author":"H Faris","year":"2019","unstructured":"Faris H, Mirjalili S, Aljarah I (2019) Automatic selection of hidden neurons and weights in neural networks using grey wolf optimizer based on a hybrid encoding scheme. Int J Mach Learn Cybern 10(10):2901\u20132920","journal-title":"Int J Mach Learn Cybern"},{"issue":"1","key":"1566_CR22","first-page":"1","volume":"31","author":"T Fawcett","year":"2004","unstructured":"Fawcett T (2004) Roc graphs: notes and practical considerations for researchers. Mach Learn 31(1):1\u201338","journal-title":"Mach Learn"},{"issue":"6","key":"1566_CR23","doi-asserted-by":"publisher","first-page":"2931","DOI":"10.1111\/j.1540-6261.2007.01297.x","volume":"62","author":"LA Franzen","year":"2007","unstructured":"Franzen LA, Rodgers KJ, Simin TT (2007) Measuring distress risk: the effect of r&d intensity. J Financ 62(6):2931\u20132967","journal-title":"J Financ"},{"issue":"1","key":"1566_CR24","doi-asserted-by":"publisher","first-page":"269","DOI":"10.1111\/j.1540-6261.1985.tb04949.x","volume":"40","author":"FNH Frydman","year":"1985","unstructured":"Frydman FNH et al (1985) Introducing recursive partitioning for financial classification: the case of financial distress. J Financ 40(1):269\u2013291","journal-title":"J Financ"},{"key":"1566_CR25","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1016\/j.jcorpfin.2018.06.003","volume":"51","author":"E Garcia-Appendini","year":"2018","unstructured":"Garcia-Appendini E (2018) Financial distress and competitors\u2019 investment. J Corp Financ 51:182\u2013209","journal-title":"J Corp Financ"},{"issue":"1","key":"1566_CR26","doi-asserted-by":"publisher","first-page":"236","DOI":"10.1016\/j.ejor.2014.08.016","volume":"241","author":"R Geng","year":"2015","unstructured":"Geng R, Bose I, Chen X (2015) Prediction of financial distress: an empirical study of listed Chinese companies using data mining. Eur J Oper Res 241(1):236\u2013247","journal-title":"Eur J Oper Res"},{"key":"1566_CR27","doi-asserted-by":"crossref","unstructured":"Gentry JA, Newbold P, Whitford DT (1985) Classifying bankrupt firms with funds flow components. J Account Res 146\u2013160","DOI":"10.2307\/2490911"},{"issue":"2","key":"1566_CR28","doi-asserted-by":"publisher","first-page":"315","DOI":"10.1016\/0304-405X(90)90059-9","volume":"27","author":"SC Gilson","year":"1990","unstructured":"Gilson SC, John K, Lang LH (1990) Troubled debt restructurings: an empirical study of private reorganization of firms in default. J Financ Econ 27(2):315\u2013353","journal-title":"J Financ Econ"},{"issue":"3","key":"1566_CR29","doi-asserted-by":"publisher","first-page":"1351","DOI":"10.1007\/s10489-020-01904-z","volume":"51","author":"T Goel","year":"2021","unstructured":"Goel T, Murugan R, Mirjalili S, Chakrabartty DK (2021) Optconet: an optimized convolutional neural network for an automatic diagnosis of COVID-19. Appl Intell 51(3):1351\u20131366","journal-title":"Appl Intell"},{"issue":"6","key":"1566_CR30","doi-asserted-by":"publisher","first-page":"3787","DOI":"10.1109\/TII.2021.3110517","volume":"18","author":"J Guo","year":"2021","unstructured":"Guo J, Liu Z, Chen C, Zhang T, Wang L, Fan K (2022) An efficient inspection system based on broad learning: nondestructively estimating cement compressive strength with internal factors. IEEE Trans Ind Inform 18(6):3787\u20133798","journal-title":"IEEE Trans Ind Inform"},{"key":"1566_CR31","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2020.106698","volume":"96","author":"J Guo","year":"2020","unstructured":"Guo J, Wang L, Fan K, Yang B (2020) An efficient model for predicting setting time of cement based on broad learning system. Appl Soft Comput 96:106698","journal-title":"Appl Soft Comput"},{"issue":"1","key":"1566_CR32","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/B:RAST.0000013627.90884.b7","volume":"9","author":"SA Hillegeist","year":"2004","unstructured":"Hillegeist SA, Keating EK, Cram DP, Lundstedt KG (2004) Assessing the probability of bankruptcy. Rev Acc Stud 9(1):5\u201334","journal-title":"Rev Acc Stud"},{"issue":"10","key":"1566_CR33","doi-asserted-by":"publisher","first-page":"3414","DOI":"10.1287\/mnsc.2016.2514","volume":"63","author":"J Hilscher","year":"2017","unstructured":"Hilscher J, Wilson M (2017) Credit ratings and credit risk: is one measure enough? Manag Sci 63(10):3414\u20133437","journal-title":"Manag Sci"},{"key":"1566_CR34","unstructured":"Hopwood W, McKeown J, Mutchler J (1989) A test of the incremental explanatory power of opinions qualified for consistency and uncertainty. Account Rev, 28\u201348"},{"key":"1566_CR35","doi-asserted-by":"crossref","unstructured":"Horta\u00e7su FN: A. et al (2013) Indirect costs of financial distress in durable goods industries: The case of auto manufacturers. The Review of Financial Studies 26(5):1248\u20131290","DOI":"10.1093\/rfs\/hht006"},{"key":"1566_CR36","doi-asserted-by":"crossref","unstructured":"Hu, Jin (2018) Social burden and the dynamics of corporate financial distress: an investigation based on the st rules. Account Res 11:28\u201335","DOI":"10.1007\/978-3-319-67355-4_3"},{"issue":"2","key":"1566_CR37","doi-asserted-by":"publisher","first-page":"434","DOI":"10.1016\/j.eswa.2006.05.006","volume":"33","author":"FNZ Hua","year":"2007","unstructured":"Hua FNZ et al (2007) Predicting corporate financial distress based on integration of support vector machine and logistic regression. Expert Syst Appl 33(2):434\u2013440","journal-title":"Expert Syst Appl"},{"issue":"3","key":"1566_CR38","doi-asserted-by":"publisher","first-page":"256","DOI":"10.1016\/j.jaccpubpol.2010.10.003","volume":"30","author":"S Ibrahim","year":"2011","unstructured":"Ibrahim S, Lloyd C (2011) The association between non-financial performance measures in executive compensation contracts and earnings management. J Account Public Policy 30(3):256\u2013274","journal-title":"J Account Public Policy"},{"key":"1566_CR39","first-page":"19","volume":"05","author":"Jiang","year":"2001","unstructured":"Jiang, Sun, Jiang & Sun (2001) Governance weakening and financial distress: a forecasting model. Nankai Bus Rev 05:19\u201325 (in Chinese)","journal-title":"Nankai Bus Rev"},{"issue":"1","key":"1566_CR40","first-page":"131","volume":"44","author":"ZM Jiang","year":"2009","unstructured":"Jiang ZM, Lu Z, Chen C (2009) Managerial overconfidence, firm expansion and financial distress. Econ Res J 44(1):131\u2013143 (in Chinese)","journal-title":"Econ Res J"},{"issue":"4","key":"1566_CR41","doi-asserted-by":"publisher","first-page":"1011","DOI":"10.2308\/accr.2004.79.4.1011","volume":"79","author":"S Jones","year":"2004","unstructured":"Jones S, Hensher DA (2004) Predicting firm financial distress: a mixed logit model. Account Rev 79(4):1011\u20131038","journal-title":"Account Rev"},{"key":"1566_CR42","first-page":"1","volume":"13","author":"MS Kiran","year":"2021","unstructured":"Kiran MS, Siramkaya E, Esme E, Senkaya MN (2021) Prediction of the number of students taking make-up examinations using artificial neural networks. Int J Mach Learn Cybern 13:1\u201311","journal-title":"Int J Mach Learn Cybern"},{"issue":"1","key":"1566_CR43","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ejor.2006.08.043","volume":"180","author":"PR Kumar","year":"2007","unstructured":"Kumar PR, Ravi V (2007) Bankruptcy prediction in banks and firms via statistical and intelligent techniques-a review. Eur J Oper Res 180(1):1\u201328","journal-title":"Eur J Oper Res"},{"key":"1566_CR44","doi-asserted-by":"publisher","DOI":"10.1002\/9781119270041","volume-title":"The end of accounting and the path forward for investors and managers","author":"B Lev","year":"2016","unstructured":"Lev B, Gu F (2016) The end of accounting and the path forward for investors and managers. Wiley, Amsterdam"},{"issue":"5","key":"1566_CR45","doi-asserted-by":"publisher","first-page":"1297","DOI":"10.1007\/s13042-020-01234-z","volume":"12","author":"R Li","year":"2021","unstructured":"Li R, Wang X, Song Y, Lei L (2021) Hierarchical extreme learning machine with l21-norm loss and regularization. Int J Mach Learn Cybern 12(5):1297\u20131310","journal-title":"Int J Mach Learn Cybern"},{"issue":"2","key":"1566_CR46","doi-asserted-by":"publisher","first-page":"555","DOI":"10.1007\/s13042-020-01188-2","volume":"12","author":"Y Li","year":"2021","unstructured":"Li Y, Chai Y, Yin H, Chen B (2021) A novel feature learning framework for high-dimensional data classification. Int J Mach Learn Cybern 12(2):555\u2013569","journal-title":"Int J Mach Learn Cybern"},{"issue":"3","key":"1566_CR47","first-page":"475","volume":"50","author":"R Libby","year":"1975","unstructured":"Libby R (1975) The use of simulated decision makers in information evaluation. Account Rev 50(3):475\u2013489","journal-title":"Account Rev"},{"issue":"8","key":"1566_CR48","doi-asserted-by":"publisher","first-page":"5895","DOI":"10.1016\/j.eswa.2010.02.016","volume":"37","author":"FNH Li","year":"2010","unstructured":"Li FNH et al (2010) Predicting business failure using classification and regression tree: an empirical comparison with popular classical statistical methods and top classification mining methods. Expert Syst Appl 37(8):5895\u20135904","journal-title":"Expert Syst Appl"},{"key":"1566_CR49","first-page":"42","volume":"02","author":"Liu","year":"2004","unstructured":"Liu, He, Liu & He (2004) Research on operation failure warning of listed companies based on artificial neural network method. Account Res 02:42\u201346 (in Chinese)","journal-title":"Account Res"},{"key":"1566_CR50","doi-asserted-by":"publisher","first-page":"498","DOI":"10.1016\/j.asoc.2013.09.006","volume":"14","author":"C Liu","year":"2014","unstructured":"Liu C, Liang Y, Luan XZ, Leung KS, Chan TM, Xu ZB, Zhang H (2014) The l1\/2 regularization method for variable selection in the cox model. Appl Soft Comput 14:498\u2013503","journal-title":"Appl Soft Comput"},{"issue":"3","key":"1566_CR51","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1016\/0378-4266(77)90022-X","volume":"1","author":"D Martin","year":"1977","unstructured":"Martin D (1977) Early warning of bank failure: a logit regression approach. J Bank Financ 1(3):249\u2013276","journal-title":"J Bank Financ"},{"issue":"4","key":"1566_CR52","doi-asserted-by":"publisher","first-page":"1621","DOI":"10.2308\/accr-50983","volume":"90","author":"WJ Mayew","year":"2015","unstructured":"Mayew WJ, Sethuraman M, Venkatachalam M (2015) Md&a disclosure and the firm\u2019s ability to continue as a going concern. Account Rev 90(4):1621\u20131651","journal-title":"Account Rev"},{"issue":"4","key":"1566_CR53","doi-asserted-by":"publisher","first-page":"603","DOI":"10.1016\/j.eswa.2004.12.008","volume":"28","author":"JH Min","year":"2005","unstructured":"Min JH, Lee YC (2005) Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters. Expert Syst Appl 28(4):603\u2013614","journal-title":"Expert Syst Appl"},{"key":"1566_CR54","doi-asserted-by":"crossref","unstructured":"Namasudra S, Dhamodharavadhani S, Rathipriya R (2021) Nonlinear neural network based forecasting model for predicting COVID-19 cases. Neural Process Lett 1\u201321","DOI":"10.4314\/ahs.v21i1.26"},{"key":"1566_CR55","doi-asserted-by":"crossref","unstructured":"Ne\u0161et\u0159il J, de Mendez PO (2012) Sparsity. Algorithms and combinatorics, vol 28. Springer, pp. xxiv+\u2013457","DOI":"10.1007\/978-3-642-27875-4"},{"key":"1566_CR56","doi-asserted-by":"crossref","unstructured":"Ohlson JA (1980) Financial ratios and the probabilistic prediction of bankruptcy. J Account Res 109\u2013131","DOI":"10.2307\/2490395"},{"key":"1566_CR57","unstructured":"Pastena V, Ruland W (1986) The merger\/bankruptcy alternative. Account Rev 288\u2013301"},{"issue":"6","key":"1566_CR58","doi-asserted-by":"publisher","first-page":"1113","DOI":"10.1086\/250042","volume":"106","author":"RL Porta","year":"1998","unstructured":"Porta RL, Lopez-de Silanes F, Shleifer A, Vishny RW (1998) Law and finance. J Polit Econ 106(6):1113\u20131155","journal-title":"J Polit Econ"},{"key":"1566_CR59","unstructured":"Ruder S (2016) An overview of gradient descent optimization algorithms. arXiv preprint arXiv:1609.04747"},{"issue":"1","key":"1566_CR60","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/S0304-405X(02)00149-6","volume":"66","author":"A Shleifer","year":"2002","unstructured":"Shleifer A, Wolfenzon D (2002) Investor protection and equity markets. J Financ Econ 66(1):3\u201327","journal-title":"J Financ Econ"},{"issue":"3","key":"1566_CR61","doi-asserted-by":"publisher","first-page":"491","DOI":"10.1007\/s13042-016-0548-5","volume":"9","author":"P Singh","year":"2018","unstructured":"Singh P (2018) Rainfall and financial forecasting using fuzzy time series and neural networks based model. Int J Mach Learn Cybern 9(3):491\u2013506","journal-title":"Int J Mach Learn Cybern"},{"issue":"1","key":"1566_CR62","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.knosys.2006.11.003","volume":"21","author":"J Sun","year":"2008","unstructured":"Sun J, Li H (2008) Data mining method for listed companies financial distress prediction. Knowl-Based Syst 21(1):1\u20135","journal-title":"Knowl-Based Syst"},{"issue":"1","key":"1566_CR63","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","volume":"58","author":"R Tibshirani","year":"1996","unstructured":"Tibshirani R (1996) Regression shrinkage and selection via the lasso. J Roy Stat Soc: Ser B (Methodol) 58(1):267\u2013288","journal-title":"J Roy Stat Soc: Ser B (Methodol)"},{"key":"1566_CR64","first-page":"38","volume":"6","author":"ZL Wang","year":"2017","unstructured":"Wang ZL, He X (2017) Comparative study of the effect of financial distress pre warning model between the consolidated and parent financial statement. Account Res 6:38\u201344","journal-title":"Account Res"},{"issue":"10","key":"1566_CR65","doi-asserted-by":"publisher","first-page":"2631","DOI":"10.1007\/s13042-018-0850-5","volume":"10","author":"J Wang","year":"2019","unstructured":"Wang J, Anisetti M, Jeon G (2019) Reconstruction of missing color-channel data using a three-step back propagation neural network. Int J Mach Learn Cybern 10(10):2631\u20132642","journal-title":"Int J Mach Learn Cybern"},{"issue":"10","key":"1566_CR66","doi-asserted-by":"publisher","first-page":"2209","DOI":"10.1007\/s13042-020-01110-w","volume":"11","author":"X Wang","year":"2020","unstructured":"Wang X, Wang W, Men C (2020) An adaptive kernel sparse representation-based classification. Int J Mach Learn Cybern 11(10):2209\u20132219 (in Chinese)","journal-title":"Int J Mach Learn Cybern"},{"key":"1566_CR67","first-page":"99","volume":"11","author":"Wu","year":"2005","unstructured":"Wu, Wu, Wu & Wu (2005) A study on prediction model for changes of financial status based on value-creation and corporate governance. Econ Res J 11:99\u2013110 (in Chinese)","journal-title":"Econ Res J"},{"issue":"9","key":"1566_CR68","doi-asserted-by":"publisher","first-page":"1569","DOI":"10.3390\/app8091569","volume":"8","author":"S Wu","year":"2018","unstructured":"Wu S, Jiang H, Shen H, Yang Z (2018) Gene selection in cancer classification using sparse logistic regression with l1\/2 regularization. Applied Sciences 8(9):1569","journal-title":"Applied Sciences"},{"issue":"7","key":"1566_CR69","doi-asserted-by":"publisher","first-page":"1765","DOI":"10.1007\/s13042-018-0853-2","volume":"10","author":"C Zhang","year":"2019","unstructured":"Zhang C, Zhou Y, Guo J, Wang G, Wang X (2019) Research on classification method of high-dimensional class-imbalanced datasets based on svm. Int J Mach Learn Cybern 10(7):1765\u20131778","journal-title":"Int J Mach Learn Cybern"},{"issue":"03","key":"1566_CR70","first-page":"412","volume":"40","author":"H Zhang","year":"2010","unstructured":"Zhang H, Wang Y, Chang X, Xu Z (2010) $$l_{1\/2}$$ regularization. Chin Sci Inform Sci 40(03):412\u2013422","journal-title":"Chin Sci Inform Sci"},{"key":"1566_CR71","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1016\/j.knosys.2015.04.017","volume":"85","author":"L Zhou","year":"2015","unstructured":"Zhou L, 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","journal-title":"Knowl-Based Syst"},{"key":"1566_CR72","doi-asserted-by":"crossref","unstructured":"Zmijewski ME (1984) Methodological issues related to the estimation of financial distress prediction models. J Account Res 59\u201382","DOI":"10.2307\/2490859"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-022-01566-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-022-01566-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-022-01566-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,9]],"date-time":"2025-04-09T12:07:11Z","timestamp":1744200431000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-022-01566-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,27]]},"references-count":72,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2022,7]]}},"alternative-id":["1566"],"URL":"https:\/\/doi.org\/10.1007\/s13042-022-01566-y","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,4,27]]},"assertion":[{"value":"20 December 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 April 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 April 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no relevant financial or non-financial interests to disclose.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}