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題名 GA-SVM組合式信用風險財務危機模型之研究
作者 曾淑峰;江俊豪
Tseng,Shu-feng;Chiang,Chun-hao
關鍵詞 支持向量機 ; 遺傳演算法 ; 信用風險模型
     ;Support Vector Machine ; Genetic Algorithm ; Credit Risk Model
日期 2008-03
上傳時間 17-Jan-2009 16:30:34 (UTC+8)
摘要 金融機構客戶的信用風險預測一直是很重要的議題，其與金融機構決策與獲利息息相關，至今已有許多預測模型被應用於信用風險預測，包括最近提出的Support Vector Machine(SVM)演算法，而SVM方法亦被拿來與Neural Network(NN)、Logistic Regression(LR)比較，顯示有較佳的結果。Genetic Algorithm(GA)演算法長久以來搭配其他預測技術如:NN，但GA與SVM結合在一起的相關研究並不多見，特別在於信用風險預測。本研究之目的在發展一套GA-SVM的預測方法，使用subset selection及parameter optimization，以基層金融機構客戶破產預測為例，並與一般常用的金融機構放款客戶破產預測方法做一比較。
Credit risk prediction is an important and widely studied topic since it has significant impact on financial institution lending decision and profitability. There have been many predication models applied to this area, including the recently proposed Support Vector Machine (SVM). The SVM-based method has been compared with other methods, such as neural network (NN) and logistic regression, and shown better results. For a long time, genetic algorithm (GA) has been applied in conjunction with other prediction techniques such as NN. However, few studies have dealt with the integration of GA and SVM, especially on credit risk prediction. The study proposes a method of improving SVM performance from subset selection and parameter optimization by combining the GA technique. Using the sample data for savings and loan association, the prediction power of the proposed GA-SVM approach is compared with other methods on customer bankruptcy prediction.
關聯 臺灣金融財務季刊,9(1),1-25
資料類型 article
dc.creator (作者) 曾淑峰;江俊豪zh_TW
dc.creator (作者) Tseng,Shu-feng;Chiang,Chun-hao-
dc.date (日期) 2008-03en_US
dc.date.accessioned 17-Jan-2009 16:30:34 (UTC+8)-
dc.date.available 17-Jan-2009 16:30:34 (UTC+8)-
dc.date.issued (上傳時間) 17-Jan-2009 16:30:34 (UTC+8)-
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/27312-
dc.description.abstract (摘要) 金融機構客戶的信用風險預測一直是很重要的議題，其與金融機構決策與獲利息息相關，至今已有許多預測模型被應用於信用風險預測，包括最近提出的Support Vector Machine(SVM)演算法，而SVM方法亦被拿來與Neural Network(NN)、Logistic Regression(LR)比較，顯示有較佳的結果。Genetic Algorithm(GA)演算法長久以來搭配其他預測技術如:NN，但GA與SVM結合在一起的相關研究並不多見，特別在於信用風險預測。本研究之目的在發展一套GA-SVM的預測方法，使用subset selection及parameter optimization，以基層金融機構客戶破產預測為例，並與一般常用的金融機構放款客戶破產預測方法做一比較。-
dc.description.abstract (摘要) Credit risk prediction is an important and widely studied topic since it has significant impact on financial institution lending decision and profitability. There have been many predication models applied to this area, including the recently proposed Support Vector Machine (SVM). The SVM-based method has been compared with other methods, such as neural network (NN) and logistic regression, and shown better results. For a long time, genetic algorithm (GA) has been applied in conjunction with other prediction techniques such as NN. However, few studies have dealt with the integration of GA and SVM, especially on credit risk prediction. The study proposes a method of improving SVM performance from subset selection and parameter optimization by combining the GA technique. Using the sample data for savings and loan association, the prediction power of the proposed GA-SVM approach is compared with other methods on customer bankruptcy prediction.-
dc.format application/en_US
dc.language zh-TWen_US
dc.language en-USen_US
dc.language.iso en_US-
dc.relation (關聯) 臺灣金融財務季刊,9(1),1-25en_US
dc.subject (關鍵詞) 支持向量機 ; 遺傳演算法 ; 信用風險模型
     ;Support Vector Machine ; Genetic Algorithm ; Credit Risk Model		-
dc.title (題名) GA-SVM組合式信用風險財務危機模型之研究en_US
dc.type (資料類型) articleen