政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/27312
English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  Items with full text/Total items : 113313/144292 (79%)
Visitors : 50948921      Online Users : 998
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
    •  Adv. Search
    Home ‧ Login ‧ Upload ‧ Help ‧ About ‧ Administer Goto mobile version


    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/27312


    Title: GA-SVM組合式信用風險財務危機模型之研究
    Authors: 曾淑峰;江俊豪
    Tseng,Shu-feng;Chiang,Chun-hao
    Keywords: 支持向量機;遺傳演算法;信用風險模型;Support Vector Machine;Genetic Algorithm;Credit Risk Model
    Date: 2008-03
    Issue Date: 2009-01-17 16:30:34 (UTC+8)
    Abstract: 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.
    Relation: 臺灣金融財務季刊,9(1),1-25
    Data Type: article
    Appears in Collections:[Department of MIS] Periodical Articles

    Files in This Item:

    File SizeFormat
    index.html0KbHTML21549View/Open


    All items in 政大典藏 are protected by copyright, with all rights reserved.


    社群 sharing

    著作權政策宣告 Copyright Announcement
    1.本網站之數位內容為國立政治大學所收錄之機構典藏，無償提供學術研究與公眾教育等公益性使用，惟仍請適度，合理使用本網站之內容，以尊重著作權人之權益。商業上之利用，則請先取得著作權人之授權。
    The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

    2.本網站之製作，已盡力防止侵害著作權人之權益，如仍發現本網站之數位內容有侵害著作權人權益情事者，請權利人通知本網站維護人員(nccur@nccu.edu.tw)，維護人員將立即採取移除該數位著作等補救措施。
    NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback