Credit Scoring Using the Hybrid Neural Discriminant Technique
學年 91
學期 1
出版（發表）日期 2002-09-01
作品名稱 Credit Scoring Using the Hybrid Neural Discriminant Technique
作品名稱（其他語言）
著者 陳怡妃; Lee, T. S.; Chiu, C. C.; Lu, C. J.
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 Applications 23(3), pp.245-254
摘要 Credit scoring has become a very important task as the credit industry has been experiencing double-digit growth rate during the past few decades. The artificial neural network is becoming a very popular alternative in credit scoring models due to its associated memory characteristic and generalization capability. However, the decision of network's topology, importance of potential input variables and the long training process has often long been criticized and hence limited its application in handling credit scoring problems. The objective of the proposed study is to explore the performance of credit scoring by integrating the backpropagation neural networks with traditional discriminant analysis approach. To demonstrate the inclusion of the credit scoring result from discriminant analysis would simplify the network structure and improve the credit scoring accuracy of the designed neural network model, credit scoring tasks are performed on one bank credit card data set. As the results reveal, the proposed hybrid approach converges much faster than the conventional neural networks model. Moreover, the credit scoring accuracies increase in terms of the proposed methodology and outperform traditional discriminant analysis and logistic regression approaches.
關鍵字 Credit scoring;Discriminant analysis;Neural networks;Model basis
語言 en
ISSN 0957-4174 1873-6793
期刊性質 國外
收錄於 SCI
產學合作
通訊作者
審稿制度 否
國別 GBR
公開徵稿
出版型式 ,電子版,紙本
相關連結

機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/64922 )

機構典藏連結
SDGS 優質教育,負責任的消費與生產