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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 陳國泰 | |
| dc.contributor.author | Yi-Chien Chiang | en |
| dc.contributor.author | 江怡蒨 | zh_TW |
| dc.date.accessioned | 2021-06-15T01:16:39Z | - |
| dc.date.available | 2019-12-01 | |
| dc.date.copyright | 2009-08-06 | |
| dc.date.issued | 2009 | |
| dc.date.submitted | 2009-07-28 | |
| dc.identifier.citation | 一、中文文獻
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Statistical Classification Methods in Consumer Credit Scoring: A Review. Journal of the Royal Statistical Society, Series A-Statistics in Society 160(3): 523-541. Henley, W. E. 1995. Statistical Aspects of Credit Scoring. Dissertation, The Open University, Milton Keynes, UK. Huang, C. L., M. C. Chen, and C. J. Wang. 2007. Credit Scoring with a Data Mining Approach Based on Support Vector Machines. Expert Systems with Applications 33(4): 847-856. Huang, S. C. 2009.Integrating Nonlinear Graph Based Dimensionality Reduction Schemes with SVMs for Credit Rating Forecasting. Expert Systems with Applications 36(4): 7515-7518. Huang Z, H. Chen, C. J. Hsu, W. H. Chen, and S. Wu. 2004. Credit Rating Analysis with Support Vector Machines and Neural Networks: A Market Comparative Study [Special issue: Data Mining for Financial Decision Making]. Decision Support Systems 37(4): 543-558. 67 Johnson, R. W. 1992. Legal, Social and Economic Issues Implementing Scoring in the US. In: Thomas, L. C. Crook, J. N., and Edelman, D. B. (Eds.), Credit Scoring and Credit Control, Oxford University Press, Oxford. Klayman, J.1988.Cue Discovery in Probabilistic Environments: Uncertainty and Experimentation. Learning memory and cognition 14(2): 317-330. Kohavi, R., and G. H. John. 1997. Wrappers for Feature Subset Selection. Artificial Intelligence 97(1-2): 273-324. Lee, Y. C. 2007. Application of Support Vector Machines to Corporate Credit Rating Prediction. Expert Systems with Applications 33(1): 67-74. Li, S. T., W. Shiue, and M. H. Huang. 2006. The Evaluation of Consumer Loans Using Support Vector Machines. Expert Systems with Applications 30(4): 772-782. Liu, H., and M. Hiroshi. 1998. Feature Selection for Knowledge Discovery and Data Mining. Kluwer Academic. Malhotra, R., and D. K. Malhotra. 2002. Differentiating between Good Credits and Bad Credits Using Neuro-Fuzzy Systems. European Journal of Operational Research 136(1): 190–211. Ong, C. S., J. J. Huang, and G. H. Tzeng. 2005. Building Credit Scoring Models Using Genetic Programming. Expert Systems with Applications 29(1): 41–47. Orgler, Y. E. 1970. A Credit Scoring Model for Commercial. Journal of Money, Credit 68 and Banking 2(4): 435-445. Reichert, A. K., C. C. Cho, and G. M. Wagner. 1983. An Examination of the Conceptual Issues Involved in Developing Credit-Scoring Models. Journal of Business and Economic Statistics 1(2): 101–114. Schumann, M. 2001. New Issues in Credit Scoring Application. Institut für Wirtschaftsinformatik. Salcedo-Sanz S., M. DePrado-Cumplido, M. J. Segovia-Vargas, F. Perez-Cruz, and C. Bousono-Calzon. 2004. Feature Selection Methods Involving SVMs for Prediction of Insolvency in Non-life Insurance Companies. Intelligent Systems in Accounting, Finance and Management 12: 261-281. Schebesch, K. B., and R. Stecking. 2005. Support Vector Machines for Classifying and Describing Credit Applicants: Detecting Typical and Critical Regions. 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Statistical Learning Theory. Chichester, UK:wiler. West, D. 2000. Neural Network Credit Scoring Models. Computers and Operations Research 27(11–12): 1131–1152. Xu, X., C. Zhou, and Z. Wang. 2009. Credit Scoring Algorithm Based on Link Analysis Ranking with Support Vector Machines. Expert Systems with Applications 36(2-2): 2625-2632. Yeh, I. C., and C. H. Lien. 2009. The Comparisons of Data Mining Techniques for the Predictive Accuracy of Probability of Default of Credit Card Clients. Expert Systems with Applications 36(2-1): 2473-2480. Yukun, B., and Z. Liu. 2006. A Fast Grid Search Method in Support Vector Regression Forecasting Time Series. Lecture Notes in Computer Science, Intelligent Data Engineering and Automated Learning . | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/42573 | - |
| dc.description.abstract | 近年來接連發生的金融危機迫使我們尋求更好的信用評等方式。在經濟發展
的過程中，汽車貸款扮演相當重要的信用借貸角色，對於消費者銀行與汽車車商 而言，判斷汽車貸款違約率是相當的重要。對放款者而言，一方面必須承擔經過 核准的汽車貸款違約所產生重大的損失；另一方面，若錯誤拒絕可以接受的貸款， 放款者則會喪失利益。 過去數十年間，許多研究針對信用評估議題提出新的模型，包括羅吉斯迴歸、 專家系統、資料探勘等。本研究利用某汽車貸款公司所提供的資料，應用「支援 向量機」分析資料，以建立汽車貸款違約之預測模型。 本研究藉由「格子點搜尋演算法」調整核心核數，並以「過濾法」與「包裝 法」進行特徵篩選，結果發現以五個特徵值所組成的分類器具有最高的預測分類 率，可達77.43%，並提出向後逐步選取法為主的羅吉斯迴歸模型，其預測分類率 可達75.70%。研究結果顯示：(1)就預測分類能力，支援向量機較羅吉斯迴歸佳。 (2)考量過多的資訊不一定能夠產生較佳的預測分類力。 | zh_TW |
| dc.description.abstract | The recent financial crises have called for better credit evaluation. Traditionally,
car loans constitute an important portion of credit lending for an economy. Determining the default probability of a car loan is a major task for consumer banking and automobile sales companies. On the one hand, the lender will suffer a loss if a granted car loan eventually defaults; on the other hand, the lender will lose a potential gain if a good loan prospect is mistakenly rejected. Previous studies have developed models for credit evaluation. These models include regression analysis, expert systems, and data mining techniques. Using data from an automobile company, this study applies support vector machines to build classifiers for car loan default prediction. By adopting the grid search approach to adjust kernel parameters and using the filter method and wrapper method to select features, this study find that a classifier with just 5 features possess the best classification power. The classification accuracy rate is 77.43%. By comparison, a step-wise logistic regression model has a classification accuracy rate of 75.70%. The results show that (1) support vector machines have better classification power than logistic regression, and (2) considering more factors does not necessary result in better classification. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-15T01:16:39Z (GMT). No. of bitstreams: 1 ntu-98-R96722036-1.pdf: 567599 bytes, checksum: 20363aab3f8146c7c39e03087300de27 (MD5) Previous issue date: 2009 | en |
| dc.description.tableofcontents | 謝辭…………………………………………………………………………………..…i
中文摘要……………………………………………………………………………….ii 英文摘要………………………………………………………………………………iii 第一章 緒論………………………………………………………………………… 1 第一節 研究背景與動機……………………………………………………… 1 第二節 研究架構……………………………………………………………… 3 第二章 文獻探討…………………………………………………………………… 5 第一節 汽車貸款……………………………………………………………… 5 第二節 信用評估……………………………………………………….………9 第三節 資料探勘………………………………………………………………17 第三章 研究方法……………………………………………………………………22 第一節 支援向量機……………………………………………………………22 第二節 特徵篩選………………………………………………………………26 第三節 核心參數………………………………………………………………28 第四節 交叉驗證………………………………………………………………29 第五節 變數定義………………………………………………………………31 第四章 實證結果與分析……………………………………………………………37 第一節 變數分析………………………………………………………………37 第二節 分析流程………………………………………………………………44 第三節 預測分類率分析………………………………………………………57 第五章 結論、限制與建議…………………………………………………………60 第一節 研究結論………………………………………………………………60 第二節 研究限制………………………………………………………………61 第三節 研究建議………………………………………………………………62 參考文獻………………………………………………………………………………63 圖目錄 圖 1-1：研究流程圖…………………………………………………………………… 4 圖3-1：3-1 SVM 二維分類圖…………………………………………………………23 圖3-2：SVM 特徵值篩選-過濾法…………………………………………………….27 圖3-3：SVM 特徵值篩選-包裝法…………………………………………….………27 圖3-4：SVM 交叉驗證資料切割圖……………………………………………………29 圖3-5：低度適應與過度適應…………………………………………………………30 圖4-1：商品價格長條圖…………………………………………..…………………..40 圖4-2：貸款金額長條圖………………………………………………….……………41 圖4-3：自備款長條圖…………………………………………………..……………..41 圖4-4：每期支付金額長條……………………………………………….……………42 圖4-5：分析流程圖………………………………………………………………….…46 圖4-6：Grid Search 前各組最大預測分類率折線圖………………………………….50 圖4-7：Grid Search 後最大預測分類率折線圖………………………………………..53 表目錄 表2-1：信用評等評估方式比較表……………………………………………………14 表2-2：資料探勘運用在信用等評等………………….………………………………18 表2-3：SVM 運用在信用評等………………….……………..………………………19 表2-4：SVM 運用在信用評等-續………………….…………………………………20 表3-1：變數定義表……………………………………………………………………35 表3-2：變數定義表-續………………………………………………….…………..…36 表4-1：離散型變數逾期狀況表………………………………………………………39 表4-2：連續型變數敘述統計表………………………………………………………43 表4-3：變數F-score 表………………………………………………….………..……48 表4-4：Grid Search 前最大預測分類率…………………………………………..……49 表4-5：Grid Search 前最大預測分類率－組合表……………………………………51 表4-6：表4-6：Grid Search 後最大預測分類率58……………………………………53 表4-7：Grid Search 後最大預測分類率－組合表………………………………..……54 表4-8 向後逐步選取法(Backward)最佳組合………………...………………………56 表4-9：訓練模型與測試資料預測分類率…………………………………………….57 表4-10：SVM 與Logistic 迴歸模型比較表…………………………………………..58 | |
| dc.language.iso | zh-TW | |
| dc.subject | 機 | zh_TW |
| dc.subject | 吉斯迴歸 | zh_TW |
| dc.subject | 支援向量 | zh_TW |
| dc.subject | 汽車 | zh_TW |
| dc.subject | 貸款 | zh_TW |
| dc.subject | 違約 | zh_TW |
| dc.subject | 資訊品質 | zh_TW |
| dc.subject | SVMs | en |
| dc.subject | information quality | en |
| dc.subject | default | en |
| dc.subject | car loan | en |
| dc.subject | Support Vector Machines | en |
| dc.subject | logistic regression | en |
| dc.title | 應用支援向量機於汽車貸款違約之預測 | zh_TW |
| dc.title | Applying Support Vector Machines to Predict
Car Loan Defaults | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 97-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 林世銘,黃美祝 | |
| dc.subject.keyword | 支援向量,機,羅,吉斯迴歸,汽車,貸款,違約,資訊品質, | zh_TW |
| dc.subject.keyword | Support Vector Machines,SVMs,logistic regression,car loan,default,information quality, | en |
| dc.relation.page | 69 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2009-07-28 | |
| dc.contributor.author-college | 管理學院 | zh_TW |
| dc.contributor.author-dept | 會計學研究所 | zh_TW |
| 顯示於系所單位： | 會計學系 | |
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