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Improving a neuro-fuzzy classifier using exploratory factor analysis.

, , , and . Int. J. Intell. Syst., 15 (8): 785-800 (2000)

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Improving a neuro-fuzzy classifier using exploratory factor analysis., , , and . Int. J. Intell. Syst., 15 (8): 785-800 (2000)The economy and loss given default: evidence from two UK retail lending data sets., , and . JORS, 65 (3): 363-375 (2014)A Modeling Approach to KBS Verification., , , and . EUROVAV, page 155-171. ADERIAS-LIA, Universite de Savoie, (1995)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)Specifying Process-Aware Access Control Rules in SBVR., , and . RuleML, volume 4824 of Lecture Notes in Computer Science, page 39-52. Springer, (2007)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)Decision diagrams in machine learning: an empirical study on real-life credit-risk data., , , and . Expert Syst. Appl., 27 (2): 257-264 (2004)An experimental comparison of classification algorithms for imbalanced credit scoring data sets., and . Expert Syst. Appl., 39 (3): 3446-3453 (2012)