English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  Items with full text/Total items : 96274/126892 (76%)
Visitors : 32321299      Online Users : 379
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
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    政大機構典藏 > 商學院 > 金融學系 > 期刊論文 >  Item 140.119/135850
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/135850


    Title: 基於集成學習框架之信用違約預測-以信用卡客戶為例
    Authors: 黃立新
    Huang, Li-Xin
    江彌修
    胡聚男
    陳靜怡
    Contributors: 金融博六
    Keywords: 信用風險;違約風險;信用卡客戶;集成學習;機器學習
    Credit Risk;Default Risk;Credit Card Clients;Ensemble Learning;Machine Learning
    Date: 2021.01
    Issue Date: 2021-06-17 15:42:46 (UTC+8)
    Abstract: 藉由堆疊 (Stacking) 與勻合 (Blending) 學習器 (base estimators) 所產生的異質 性集成學習(Heterogeneous Ensemble Learning)框架,本文建構消費金融信用卡客 戶之違約風險預警模型。採用 Yeh and Lien (2009) 的資料集,我們的實證結果顯 示,堆疊與勻合集成皆能有效降低誤判信用違約客戶為正常的型二誤差。尤其當 輔以適當的學習器挑選策略,堆疊集成的綜合辨別能力呈現一定的泛化優越成效, 明顯勝出任何非集成的單一學習器。更加地,輔以學習器挑選策略的堆疊集成能 夠提高模型識別違約客戶的準確率 (F1 值),且在增進識別違約客戶能力的同時 有效降低誤判正常客戶為違約的分類誤差 (AUC 值)。
    Based on Heterogeneous Ensemble Learning that allows for the Stacking and Blending of base learners of distinct types, in this study we construct an ensemble-learning assisted credit-risk prediction model in an attempt to prewarn consumer banks of their credit card holders’ possibility of default. Using the dataset as in Yeh and Lien (2009), our empirical results show that ensemble learning models that exploit either Stacking or Blending can effectively reduce the Type II error in mis-judging defaulted entities as normal. In particular, when equipped with a learner-selection strategy, heterogeneous ensemble learners that exploit Stacking tend to exhibit superior predictive power over all single base learners. Furthermore, ensemble learners with Stacking are found to be capable of improving the rate of accuracy in nailing down defaulted entities (F1-score); they demonstrate the ability to identify credit-critical customers while at the same time reduce the possibility of misjudging normal customers as defaulted ones (AUC-value)
    Relation: 期貨與選擇權學刊
    Data Type: article
    Appears in Collections:[金融學系] 期刊論文

    Files in This Item:

    File Description SizeFormat
    133.pdf904KbAdobe PDF19View/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
    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。
    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。
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback