A Two-stage Hybrid Credit Scoring Model Using Artificial Neural Networks and Multivariate Adaptive Regression Splines
學年 94
學期 1
出版（發表）日期 2005-09-01
作品名稱 A Two-stage Hybrid Credit Scoring Model Using Artificial Neural Networks and Multivariate Adaptive Regression Splines
作品名稱（其他語言）
著者 陳怡妃; Lee, T. S.
單位 淡江大學經營決策學系
出版者
著錄名稱、卷期、頁數 Expert Systems with Applications28(4), pp.743-752
摘要 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.
關鍵字 Credit scoring; Classification; Neural networks; Multivariate adaptive regression splines; Cross-validation
語言 en
ISSN
期刊性質 國內
收錄於 SCI
產學合作
通訊作者
審稿制度 否
國別 TWN
公開徵稿
出版型式 ,電子版,紙本
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