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研究生:陳益軒
研究生(外文):TAN YI SHIEN
論文名稱:以決策樹為基礎之支援向量機模型於信用評等之研究
論文名稱(外文):Application of Decision tree-based Support Vector Machine model for Taiwan Corporate Credit Risk Index
指導教授:白炳豐白炳豐引用關係
指導教授(外文):Ping-Feng Pai
口試委員:張炳騰洪國禎白炳豐
口試委員(外文):Ping-Teng ChangKuo-Chen.HungPing-Feng Pai
口試日期:2013-06-10
學位類別:碩士
校院名稱:國立暨南國際大學
系所名稱:資訊管理學系
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2013
畢業學年度:101
語文別:中文
論文頁數:89
中文關鍵詞:信用評等支援向量機決策樹金融TEJ信用風險指標TCRI
外文關鍵詞:Credit ratingsSVMDecision-TreefinanceTCRI
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隨著資本主義的蓬勃發展之下,各大企業以及中小企業的產生,在快速發展的全球經濟之下,各行各業中企業會透過財務槓桿的運用與銀行之間作交換,進而獲取資金轉為投資。對於金融業來說,一但市場波動發生變化,會具有無可比擬的破壞力。所以有鑑於此,在眾多的銀行機構才需要一套具有公信力的評等機制;透過第三方所評選的評等機制,可以提供給銀行業對每家企業具有基本放款依據。在研究中主要採取決策樹為基礎之支援向量機來針對信用評等評選機制作分類,透過以決策樹為基礎的支援向量機(DT-SVM)、無向循環圖支援向量機(DAGSVM)、傳統一對一支援向量機(OVOSVM)、一對多支援向量機(OVASVM)對信用評等資料分類,藉由最佳化參數期望可以試著找尋出最佳分類模組。並透過對分類準確率作出比較,從中評選出適合之分類模型以供銀行機構做參考依據。最後在此研究中結果顯示在多類資料中,決策樹為基礎之支援向量機(DT-SVM)具有良好的分類以及效率。
Below with the rapid development of capitalism, large corporations and small and medium enterprises appearing, in the rapid development of the global economy, all walks of life in the enterprise through the use of financial leverage and exchange between banks, thereby obtaining funds into investment. For the financial industry, there have an unparalleled destructive power when market volatility . So with this in mind, many banking institutions need a credible ratings system through mechanisms such as the selection of assessment by third parties, can be given to the banking sector to every enterprise has a basic loan basis. Mainly taken in the study of decision tree based on support vector machine for credit rating evaluation system for classification, through decision tree based on support vector machine (DT-SVM) on the credit rating data classification, through optimization of parameter expectations can try to find out the optimum classification parameters. Compared to the final classification accuracy, from which the selected fit the classification of model for banking institutions to make reference. Final results show many types of data in this study, based on decision tree SVM (DT-SVM) has a excellent classification, and efficiency.
目錄
1. 緒論 1
1.1為何產生呆帳? 1
1.2 信用評等的緣起 2
2. 文獻回顧 4
2.1 Taiwan Corporate Credit Risk Index(TCRI) 4
2.2 整體學習(Ensmble learning) 8
2.3 支援向量機(SVM) 14
2.4 以決策樹為基礎之支援向量機 (DT-SVM) 18
2.5 無向循環支援向量機(DAG-SVM) 23
2.6 粒子群聚演算法(PSO) 27
2.7基因演算法(GA) 31
2.8 模擬退火法(SA) 36
2.9 約略集合理論(RST) 40
3. 方法論 45
3.1 SVM應用於線性模型 46
3.2 SVM應用非線性模型 49
3.3 以決策樹為基礎之支援向量機 (DT-SVM) 51
3.4 約略集合理論(RST) 53
3.5 基因演算法(GA) 55
3.6粒子群聚演算法 (PSO) 58
3.7 模擬退火法(SA) 61
3.8 資料轉換 64
4.研究架構及實驗結果 67
4.1 資料前處理 67
4.2 特徵選取 70
4.3分類結構及流程 75
4.4 實驗結果 76
4.5 規則模型 80
5.總結 83
References 84

表目錄
表格 1TCRI相關文獻 5
表格 2 Ensemble learning相關文獻 9
表格3 支援向量機相關文獻 16
表格4 DT-SVM相關文獻 20
表格5無向循環支援向量機相關文獻 24
表格6 粒子演算法相關文獻 29
表格7基因演算法相關文獻 33
表格8模擬退火法相關文獻 37
表格9 約略集合理論相關文獻 41
表格10 特徵屬性變數一覽表 68
表格11 研究數據分配表 69
表格12屬性近年相關文獻 70
表格13特徵屬性選取表 71
表格14 DAG-SVM - PSO 76
表格15 DAG-SVM - SA 76
表格16 DAG-SVM - GA 77
表格17 OVO-SVM 77
表格18 OVA-SVM 78
表格19 DT-SVM 78
表格20 DAG-SVM 比較表 79
表格21 各分類器比較表 79
表格22準確率分析 81
表格23覆蓋率分析 81
表格24準確率與覆蓋率分析 81
表格25規則分析 82
表格26權重最高之規則 82

圖目錄
圖1 Ensemble learning架構圖 (Breiman,1996) 9
圖2 支援向量機支援向量機結構 15
圖3 DT-SVM分多類分類示意圖 19
圖4 DT-SVM分類圖 19
圖5 DAG-SVM架構圖 24
圖6 粒子演算法示意圖 28
圖7 基因演算法示意圖 32
圖8 RST示意圖 41
圖9研究結構 45
圖10 SVM示意圖 48
圖11 RST流程圖 54
圖12 GA示意圖 57
圖13 PSO示意圖 60
圖14 SA流程圖 63
圖15 取出資料點 65
圖16擷取選擇資料 65
圖17產生規則 66
圖18 公司產業分析比例圖 69
圖19 CG ∩CGR 72
圖20 CGR ∩CR 72
圖21 CG ∩CR 73
圖22 CG∩CR∩ CGR 73
圖23特徵屬性重疊表 74
圖24流程示意圖 74
圖25分類框架 75
圖26交叉比對分析圖 79
圖27規則框架 80

References

1.Angelini, E., Di Tollo, G., & Roli, A. (2008). A neural network approach for credit risk evaluation. The Quarterly Review of Economics and Finance, 48(4), 733-755.
2.Arun Kumar, M., & Gopal, M. (2010). A hybrid SVM based decision tree. Pattern Recognition, 43(12), 3977-3987.
3.Bennett, K., & Blue, J. A. (1998). A support vector machine approach to decision trees. Paper presented at the Neural Networks Proceedings, 1998. IEEE World Congress on Computational Intelligence. The 1998 IEEE International Joint Conference on.
4.Breiman, L. (1996). Bagging predictors. Machine learning, 24(2), 123-140.
5.Chen, W.-H., & Shih, J.-Y. (2006). A study of Taiwan's issuer credit rating systems using support vector machines. Expert Systems with Applications, 30(3), 427-435. doi: 10.1016/j.eswa.2005.10.003
6.Chen, Y.-S. (2012). Classifying credit ratings for Asian banks using integrating feature selection and the CPDA-based rough sets approach. Knowledge-Based Systems, 26, 259-270. doi: 10.1016/j.knosys.2011.08.021
7.Cheng, C. H., Chen, T. L., & Wei, L. Y. (2010). A hybrid model based on rough sets theory and genetic algorithms for stock price forecasting. Information Sciences, 180(9), 1610-1629.
8.Cheng, J. H., Chen, H. P., & Lin, Y. M. (2010). A hybrid forecast marketing timing model based on probabilistic neural network, rough set and C4. 5. Expert Systems with Applications, 37(3), 1814-1820.
9.Cheng, S. Y. (2012). Substitution or complementary effects between banking and stock markets: Evidence from financial openness in Taiwan. Journal of International Financial Markets, Institutions and Money, 22(3), 508-520.
10.Chien‐Yang, Guo, Chan‐Cheng, Liu, & Fu,Chang (2010). DTSVM for Large-Scale Support Vector Machines
11.Fernandez, V. (2007). Wavelet-and SVM-based forecasts: An analysis of the US metal and materials manufacturing industry. Resources Policy, 32(1), 80-89.
12.Glover, F. (1990). Tabu search: A tutorial. Interfaces, 20(4), 74-94.
13.Goldberg, D. E., & Holland, J. H. (1988). Genetic algorithms and machine learning. Machine learning, 3(2), 95-99.
14.Hájek, P. (2011). Municipal credit rating modelling by neural networks. Decision Support Systems, 51(1), 108-118. doi: 10.1016/j.dss.2010.11.033
15.Hansheng Lei, Venu Govindaraju (2006).Half-Against-Half Multi-class Support Vector Machines. CUBS, Center for Biometrics and Sensors Department of Computer Science and Engineering State University of New York at Buffalo Amherst, NY 14260-2000, USA
16.He, X., Wang, W., Liu, X., & Ji, Y. (2012). Risk Assessment of Communication Network of Power Company Based on Rough Set Theory and Multiclass SVM. Physics Procedia, 24, 1226-1231.
17.Hosmer David, W., & Stanley, L. (2000). Applied logistic regression. John Wiley&.
18.Hsu, C. F., & Hung, H. (2009). Classification Methods of Credit Rating-A Comparative Analysis on SVM, MDA and RST. Paper presented at the Computational Intelligence and Software Engineering, 2009. CiSE 2009. International Conference on.
19.Huang, Z., Chen, H., Hsu, C.-J., Chen, W.-H., & Wu, S. (2004). Credit rating analysis with support vector machines and neural networks: a market comparative study. Decision Support Systems, 37(4), 543-558. doi: 10.1016/s0167-9236(03)00086-1
20.Hull, J., Predescu, M., & White, A. (2004). The relationship between credit default swap spreads, bond yields, and credit rating announcements. Journal of Banking & Finance, 28(11), 2789-2811. doi: 10.1016/j.jbankfin.2004.06.010
21.Hwang, R.-C., Chung, H., & Chu, C. K. (2010). Predicting issuer credit ratings using a semiparametric method. Journal of Empirical Finance, 17(1), 120-137. doi: 10.1016/j.jempfin.2009.07.007
22.Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. Paper presented at the Neural Networks, 1995. Proceedings., IEEE International Conference on.
23.Kirkpatrick, S., & Vecchi, M. (1983). Optimization by simmulated annealing. science, 220(4598), 671-680.
24.Liu, S., Chan, F. T. S., & Chung, S. (2011). A study of distribution center location based on the rough sets and interactive multi-objective fuzzy decision theory. Robotics and Computer-Integrated Manufacturing, 27(2), 426-433.
25.Marshall, A., Tang, L., & Milne, A. (2010). Variable reduction, sample selection bias and bank retail credit scoring. Journal of Empirical Finance, 17(3), 501-512. doi: 10.1016/j.jempfin.2009.12.003
26.Merton, R. C. (1973). Theory of rational option pricing. The Bell Journal of Economics and Management Science, 141-183.
27.Mona Soliman Habib (2008). Addressing Scalability Issues of Named Entity Recognition Using Multi-Class Support Vector Machines. PROCEEDINGS OF WORLD ACADEMY OF SCIENCE,ENGINEERING AND THCHNOLOGY VOLUME 27 FEBRUARY 2008 ISSN 1307-6884
28.Moustakidis, S., Theocharis, J., & Giakas, G. (2010). A fuzzy decision tree-based SVM classifier for assessing osteoarthritis severity using ground reaction force measurements. Medical engineering & physics, 32(10), 1145-1160.
29.multiclass classification. Adv Neural Inform Proc Syst 12: 547–553
30.Pawlak, Z. (1982). Rough sets. International Journal of Parallel Programming, 11(5), 341-356.
31.Platt J, Cristianini N, Shawe-Taylor J (2000) Large margin DAGs for
32.Scott, J. (1981). The probability of bankruptcy: a comparison of empirical predictions and theoretical models. Journal of Banking & Finance, 5(3), 317-344.
33.Sugumaran, V., Muralidharan, V., & Ramachandran, K. (2007). Feature selection using Decision Tree and classification through Proximal Support Vector Machine for fault diagnostics of roller bearing. Mechanical systems and signal processing, 21(2), 930-942.
34.Shu Ling Lin, 2008: of Hosmer, DW, & Lemeshow, SL, 2000 . the Applied the logistic regression (2nd ed.) New York : A Wiley, - Interscience.
35.Sun, J., & Li, H. (2012). Financial distress prediction using support vector machines: Ensemble vs individual. Applied Soft Computing.
36.U.H.-G. Kreßel ,Pairwise classification and support vector machines,B. Schölkopf, C.J.C. Burges, A.J. Smola (Eds.), Advances in kernel methods: Support vector learning, MIT Press, Cambridge, MA (1999), pp. 255–268
37.V. Vapnik. The Nature of Statistical Learning Theory. Springer Verlag, New York, 1995.
38.Wang, G., Hao, J., Ma, J., & Jiang, H. (2011). A comparative assessment of ensemble learning for credit scoring. Expert Systems with Applications, 38(1), 223-230.
39.Wang, T. C., & Chen, Y. H. (2006). Applying Rough Sets Theory to Corporate Credit Ratings. Paper presented at the Service Operations and Logistics, and Informatics, 2006. SOLI'06. IEEE International Conference on.
40.Wang, X., Shi, Z., Wu, C., & Wang, W. (2006). An improved algorithm for decision-tree-based SVM. Paper presented at the Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on.
41.WILCOX J. W., A gambler's ruin prediction of business failure using ac- counting data, Sloan Management Review, 3, 1971.
42.Yeh, C. C., Lin, F., & Hsu, C. Y. (2012). A hybrid KMV model, random forests and rough set theory approach for credit rating. Knowledge-Based Systems.
43.Zadeh, LA (1964). “Fuzzy Sets,” Memo. ERL, No. 64-44.
44.Zhang, P., Zhu, X., Shi, Y., Guo, L., & Wu, X. (2010). Robust ensemble learning for mining noisy data streams. Decision Support Systems.

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