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Kredi Kartı Taleplerinin Değerlendirilmesinde Grup ve Bireysel Kredi Puanlama Modellerinin Karşılaştırılmalı Bir Analizi

Yıl 2010, Cilt: 4 Sayı: 1, 75 - 90, 01.06.2010

Kaynakça

  • Bodur, C. ve Teker, S.. (2005). Credit Scoring of Companies: Application to the ISEM Companies.İTÜ Dergisi/b, 2(1): 25-36.
  • Breiman, L., Friedman, J. H., Olshen, R. A. ve Stone, C. J.. (1984). Classificati- on and Regression Trees. Wadsworth and Brooks/Cole, Montery.
  • Breiman, L.. (1996). Bagging Predictors. Machine Learning, 24 (3):123-140.
  • Chang, C. L. ve Chen, C. H.. (2008). Applying Decision Tree and Neural Net- work to Increase Quality of Dermatologic Diagnosis. Expert Systems with App- lications, 3(6): 4035-4041.
  • Chen, M. S., Han, J. ve Yu, P. S.. (1996). Data Mining: An Overview From a Database Perspective. IEEE Trans. Knowledge Data Engineering, 8(6): 866–883.
  • Chen, S. Y. ve Liu, X.. (2004). The Contribution of Data Mining to Informati- on Science. Journal of Information Science, 30(6): 550-558.
  • Çinko, M.. (2006). Kredi Kartı Değerlendirme Tekniklerinin Karflılafltırılması. İs- tanbul Ticaret Üniversitesi Sosyal Bilimler Dergisi, 5 (9): 143-153.
  • Crook, J. ve Banasik, J.. (2004). Does Reject Inference Really Improve the Per- formance of Application Scoring Models? Journal of Banking and Finance, 28: 857-874.
  • ki, R., Newton, J., Parzen, E. ve Winkler, R.. (1982). The Accuracy of Extrapola
  • tion (time series) Methods: Results of a Forecasting Competition. Journal of Fo
  • recasting, 1 (2): 111-153.
  • Malhotra, R. ve Malhotra, D. K.. (2002). Differentiating Between Good Credits and Bad Credits Using Neuro-Fuzzy Systems. European Journal of Operational Research, 136(1): 190–211.
  • Martens, D., Baesens, B., Van Gestel, T. ve Vanthienen, J.. (2007). Comprehen- sible Credit Scoring Models Using Rule Extraction From Support Vector Machi- nes. European Journal of Operational Research, 183(3): 1466 - 1476.
  • Olmeda, I. ve Fernandez, E.. (1997). Hybrid Classifiers for Financial Multicriteri- a Decision Making: The Case of Bankruptcy Prediction. Computational Econo- mics, 10: 317–335.
  • Ong, C. S., Huang, J. J. ve Tzeng, G. H.. (2005). Building Credit Scoring Models Using Genetic Programming.Expert Systems with Applications, 29(1): 41–47.
  • Oza, N. C.. (2006). Ensemble Data Mining Methods, Encyclopedia of Data Wa- rehousing and Mining. Idea Group Reference, pp.448-452.
  • Quinlan, J. R.. (1993). C4.5: Programs for machine learning. Morgan Kaufman, San Francisco, CA.
  • Pal, M.. (2007). Ensemble Learning with Decision Tree for Remote Sensing Clas- sification. Proceedings of World Academy of Science Engineering and Techno- logy, 26(December): 735-737.
  • Pelikan, E., De Groot, C. ve Wurtz, D.. (1992). Power Consumption in West-Bo- hemia: Improved Forecasts Decorrelating Connectionist Networks. Neural Net- work World, No.2, 701-712.
  • Perrone, M. P. ve Cooper, L. N. (1993). When Networks Disagree: Ensemble Methods for Hybrid Neural Netwoks. Neural Networks for speech and Image Processing, Chapman Hall, 126–142.
  • Schapire, R. E.. (1990). The Strength of Weak Learnability. Machine Learning, 5(2): 197-227.
  • Shen, A., Tong, R. ve Deng, Y.. (2007). Application of Classification Models on Credit Card Fraud Detection. School of Management, Graduate University of the Chinese Academy of Sciences, China, 1-4
  • Seval, B.. (1990). Kredilendirme Süreci ve Kredi Yönetimi. İ.Ü. İflletme Fakülte- si, Muhasebe Enstitüsü Yayın No.59: İstanbul.
  • Thomas, L. C.. (2000). A Survey of Credit and Behavioral Scoring: Forecasting Financial Risk of Lending to Consumers.International Journal of Forecasting, 16: 149–172.
  • Tsai, C. F. ve Wu, J. W.. (2008). Using Neural Network Ensembles for Ban- kruptcy Prediction and Credit Scoring. Expert Systems with Applications, 34(4): 2639–2649.
  • Tso, K. F. G. ve Yau, K. K. W.. (2007). Predicting Electricity Energy Consump- tion: A Comparison of Regression Analysis, Decision Tree and Neural Networks. Energy, 32: 1761–1768.
  • Vellido, A., Lisboa, P. J. G. ve Vaughan, J.. (1999). Neural Networks in Business: A Survey of Applications (1992–1998). Expert Systems with Applications, 17: 51–70.
  • Vojtek, M. ve Koãenda, E.. (2006). Credit Scoring Methods. Finance a şvûr- Czech Journal of Economics and Finance, 56: 152-167.
  • West, D.. (2000). Neural Network Credit Scoring Models. Computers and Ope- rational Research, 27: 1131–1152.
  • West, D., Dellana, S. ve Qian, J.. (2005). Neural Network Ensemble Strategi- es for Financial Decision Applications. Computers & Operations Research, 32: 2543–2559.
  • Yang, Y.. (2007). Adaptive Credit Scoring with Kernel Learning Methods. Euro- pean Journal of Operational Research, 183(3): 1521-1536.
  • Yu, L., Wang, S. Y. ve Lai, K. K.. (2005). A Novel Non-Linear Ensemble Forecas- ting Model Incorporating GLAR and ANN for Foreign Exchange Rates. Compu- ters and Operations Research, 32 (10): 2523–2541.
  • Zhao, H.. (2007). A Multi-Objective Genetic Programming Approach to Develo- ping Pareto Optimal Decision Trees. Decision Support Systems, 43: 809–826.
  • Zhou, Z. H., Wu, J. ve Tang, W.. (2002). Ensembling Neural Networks: Many Could be Better Than All. Artificial Intelligence,113377 (1-2):239-263.

Abstract - A Comparative Analysis of Individual and Ensemble Credit Scoring Techniques in Evaluating Credit Card Loan Applications

Yıl 2010, Cilt: 4 Sayı: 1, 75 - 90, 01.06.2010

Kaynakça

  • Bodur, C. ve Teker, S.. (2005). Credit Scoring of Companies: Application to the ISEM Companies.İTÜ Dergisi/b, 2(1): 25-36.
  • Breiman, L., Friedman, J. H., Olshen, R. A. ve Stone, C. J.. (1984). Classificati- on and Regression Trees. Wadsworth and Brooks/Cole, Montery.
  • Breiman, L.. (1996). Bagging Predictors. Machine Learning, 24 (3):123-140.
  • Chang, C. L. ve Chen, C. H.. (2008). Applying Decision Tree and Neural Net- work to Increase Quality of Dermatologic Diagnosis. Expert Systems with App- lications, 3(6): 4035-4041.
  • Chen, M. S., Han, J. ve Yu, P. S.. (1996). Data Mining: An Overview From a Database Perspective. IEEE Trans. Knowledge Data Engineering, 8(6): 866–883.
  • Chen, S. Y. ve Liu, X.. (2004). The Contribution of Data Mining to Informati- on Science. Journal of Information Science, 30(6): 550-558.
  • Çinko, M.. (2006). Kredi Kartı Değerlendirme Tekniklerinin Karflılafltırılması. İs- tanbul Ticaret Üniversitesi Sosyal Bilimler Dergisi, 5 (9): 143-153.
  • Crook, J. ve Banasik, J.. (2004). Does Reject Inference Really Improve the Per- formance of Application Scoring Models? Journal of Banking and Finance, 28: 857-874.
  • ki, R., Newton, J., Parzen, E. ve Winkler, R.. (1982). The Accuracy of Extrapola
  • tion (time series) Methods: Results of a Forecasting Competition. Journal of Fo
  • recasting, 1 (2): 111-153.
  • Malhotra, R. ve Malhotra, D. K.. (2002). Differentiating Between Good Credits and Bad Credits Using Neuro-Fuzzy Systems. European Journal of Operational Research, 136(1): 190–211.
  • Martens, D., Baesens, B., Van Gestel, T. ve Vanthienen, J.. (2007). Comprehen- sible Credit Scoring Models Using Rule Extraction From Support Vector Machi- nes. European Journal of Operational Research, 183(3): 1466 - 1476.
  • Olmeda, I. ve Fernandez, E.. (1997). Hybrid Classifiers for Financial Multicriteri- a Decision Making: The Case of Bankruptcy Prediction. Computational Econo- mics, 10: 317–335.
  • Ong, C. S., Huang, J. J. ve Tzeng, G. H.. (2005). Building Credit Scoring Models Using Genetic Programming.Expert Systems with Applications, 29(1): 41–47.
  • Oza, N. C.. (2006). Ensemble Data Mining Methods, Encyclopedia of Data Wa- rehousing and Mining. Idea Group Reference, pp.448-452.
  • Quinlan, J. R.. (1993). C4.5: Programs for machine learning. Morgan Kaufman, San Francisco, CA.
  • Pal, M.. (2007). Ensemble Learning with Decision Tree for Remote Sensing Clas- sification. Proceedings of World Academy of Science Engineering and Techno- logy, 26(December): 735-737.
  • Pelikan, E., De Groot, C. ve Wurtz, D.. (1992). Power Consumption in West-Bo- hemia: Improved Forecasts Decorrelating Connectionist Networks. Neural Net- work World, No.2, 701-712.
  • Perrone, M. P. ve Cooper, L. N. (1993). When Networks Disagree: Ensemble Methods for Hybrid Neural Netwoks. Neural Networks for speech and Image Processing, Chapman Hall, 126–142.
  • Schapire, R. E.. (1990). The Strength of Weak Learnability. Machine Learning, 5(2): 197-227.
  • Shen, A., Tong, R. ve Deng, Y.. (2007). Application of Classification Models on Credit Card Fraud Detection. School of Management, Graduate University of the Chinese Academy of Sciences, China, 1-4
  • Seval, B.. (1990). Kredilendirme Süreci ve Kredi Yönetimi. İ.Ü. İflletme Fakülte- si, Muhasebe Enstitüsü Yayın No.59: İstanbul.
  • Thomas, L. C.. (2000). A Survey of Credit and Behavioral Scoring: Forecasting Financial Risk of Lending to Consumers.International Journal of Forecasting, 16: 149–172.
  • Tsai, C. F. ve Wu, J. W.. (2008). Using Neural Network Ensembles for Ban- kruptcy Prediction and Credit Scoring. Expert Systems with Applications, 34(4): 2639–2649.
  • Tso, K. F. G. ve Yau, K. K. W.. (2007). Predicting Electricity Energy Consump- tion: A Comparison of Regression Analysis, Decision Tree and Neural Networks. Energy, 32: 1761–1768.
  • Vellido, A., Lisboa, P. J. G. ve Vaughan, J.. (1999). Neural Networks in Business: A Survey of Applications (1992–1998). Expert Systems with Applications, 17: 51–70.
  • Vojtek, M. ve Koãenda, E.. (2006). Credit Scoring Methods. Finance a şvûr- Czech Journal of Economics and Finance, 56: 152-167.
  • West, D.. (2000). Neural Network Credit Scoring Models. Computers and Ope- rational Research, 27: 1131–1152.
  • West, D., Dellana, S. ve Qian, J.. (2005). Neural Network Ensemble Strategi- es for Financial Decision Applications. Computers & Operations Research, 32: 2543–2559.
  • Yang, Y.. (2007). Adaptive Credit Scoring with Kernel Learning Methods. Euro- pean Journal of Operational Research, 183(3): 1521-1536.
  • Yu, L., Wang, S. Y. ve Lai, K. K.. (2005). A Novel Non-Linear Ensemble Forecas- ting Model Incorporating GLAR and ANN for Foreign Exchange Rates. Compu- ters and Operations Research, 32 (10): 2523–2541.
  • Zhao, H.. (2007). A Multi-Objective Genetic Programming Approach to Develo- ping Pareto Optimal Decision Trees. Decision Support Systems, 43: 809–826.
  • Zhou, Z. H., Wu, J. ve Tang, W.. (2002). Ensembling Neural Networks: Many Could be Better Than All. Artificial Intelligence,113377 (1-2):239-263.
Toplam 34 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Bölüm Research Article
Yazarlar

Hüseyin İnce Bu kişi benim

Bora Aktan Bu kişi benim

Yayımlanma Tarihi 1 Haziran 2010
Yayımlandığı Sayı Yıl 2010 Cilt: 4 Sayı: 1

Kaynak Göster

APA İnce, H., & Aktan, B. (2010). Kredi Kartı Taleplerinin Değerlendirilmesinde Grup ve Bireysel Kredi Puanlama Modellerinin Karşılaştırılmalı Bir Analizi. BDDK Bankacılık Ve Finansal Piyasalar Dergisi, 4(1), 75-90.