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The value of text for small business default prediction: A Deep Learning approach.

, , and . Eur. J. Oper. Res., 295 (2): 758-771 (2021)

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Specifying Process-Aware Access Control Rules in SBVR., , and . RuleML, volume 4824 of Lecture Notes in Computer Science, page 39-52. Springer, (2007)Improving a neuro-fuzzy classifier using exploratory factor analysis., , , and . Int. J. Intell. Syst., 15 (8): 785-800 (2000)A Modeling Approach to KBS Verification., , , and . EUROVAV, page 155-171. ADERIAS-LIA, Universite de Savoie, (1995)The economy and loss given default: evidence from two UK retail lending data sets., , and . JORS, 65 (3): 363-375 (2014)Exposure at default models with and without the credit conversion factor., , , and . Eur. J. Oper. Res., 252 (3): 910-920 (2016)An empirical comparison of classification algorithms for mortgage default prediction: evidence from a distressed mortgage market., and . Eur. J. Oper. Res., 249 (2): 427-439 (2016)Benchmarking Classification Models for Softwre Defect Prediction: a proposed framework and novel findings, , , and . IEEE Transactions on Software Engineering, 34 (4): 485-496 (2009)The value of text for small business default prediction: A Deep Learning approach., , and . Eur. J. Oper. Res., 295 (2): 758-771 (2021)Building Credit-Risk Evaluation Expert Systems Using Neural Network Rule Extraction and Decision Tables., , , , and . ICIS, page 159-168. Association for Information Systems, (2001)Web Usage Mining with Time Constrained Association Rules., , , and . ICEIS (2), page 343-348. (2004)