A two-stage hybrid credit scoring model using artificial neural networks and multivariate adaptive regression splines Export

@article{2005_LeeChen, abstract = {The objective of the proposed study is to explore the performance of credit scoring using a two-stage hybrid modeling procedure with artificial neural networks and multivariate adaptive regression splines (MARS). The rationale under the analyses is firstly to use MARS in building the credit scoring model, the obtained significant variables are then served as the input nodes of the neural networks model. To demonstrate the effectiveness and feasibility of the proposed modeling procedure, credit scoring tasks are performed on one bank housing loan dataset using cross-validation approach. As the results reveal, the proposed hybrid approach outperforms the results using discriminant analysis, logistic regression, artificial neural networks and MARS and hence provides an alternative in handling credit scoring tasks. (c) 2005 Elsevier Ltd. All rights reserved.}, author = {Lee, T. S. and Chen, I. F.}, citeulike-article-id = {651744}, journal = {Expert Systems with Applications}, keywords = {adaptive, business, classification, consumer, credit, cross-validation, discriminant, failure, knowledge-discovery, neural-networks, prediction, regression, risk, scoring, splines}, number = {4}, pages = {743--752}, posted-at = {2006-05-18 18:23:45}, priority = {0}, title = {A two-stage hybrid credit scoring model using artificial neural networks and multivariate adaptive regression splines}, volume = {28}, year = {2005} }

TY - JOUR ID - 2005_LeeChen L3 - citeulike-article-id:651744 N2 - The objective of the proposed study is to explore the performance of credit scoring using a two-stage hybrid modeling procedure with artificial neural networks and multivariate adaptive regression splines (MARS). The rationale under the analyses is firstly to use MARS in building the credit scoring model, the obtained significant variables are then served as the input nodes of the neural networks model. To demonstrate the effectiveness and feasibility of the proposed modeling procedure, credit scoring tasks are performed on one bank housing loan dataset using cross-validation approach. As the results reveal, the proposed hybrid approach outperforms the results using discriminant analysis, logistic regression, artificial neural networks and MARS and hence provides an alternative in handling credit scoring tasks. (c) 2005 Elsevier Ltd. All rights reserved. IS - 4 JF - Expert Systems with Applications EP - 752 TI - A two-stage hybrid credit scoring model using artificial neural networks and multivariate adaptive regression splines VL - 28 SP - 743 KW - adaptive KW - business KW - classification KW - consumer KW - credit KW - cross-validation KW - discriminant KW - failure KW - knowledge-discovery KW - neural-networks KW - prediction KW - regression KW - risk KW - scoring KW - splines AU - Lee, TS AU - Chen, IF PY - 2005/// ER -