A data driven ensemble classifier for credit scoring analysis

Expert Systems with Applications

Volumn 37, Issue 1, 2010, Pages 534-545

  Hsieh, Nan Chen ^a   Hung, Lun Ping ^b  

^a NATIONAL TAIPEI UNIVERSITY OF NURSING AND HEALTH SCIENCES   (Taiwan)

^b TECHNOLOGY AND SCIENCE INSTITUTE OF NORTHERN TAIWAN   (Taiwan)

Author keywords

Bayesian network; Class wise classification; Clustering; Credit scoring system; Ensemble classifier; Neural network

Indexed keywords

CAUSAL DIAGRAM; CLASS-WISE CLASSIFICATION; CLASSIFICATION TASKS; CLUSTERING; CONTINUOUS VALUE; CREDIT SCORING; CREDIT SCORING SYSTEM; DATA DRIVEN; DATA MINING TECHNIQUES; ENSEMBLE CLASSIFIER; ENSEMBLE CLASSIFIERS; ENSEMBLE OF CLASSIFIERS; FEATURE SELECTION; HOMOGENEOUS CLUSTER; INPUT SAMPLE; MARKOV BLANKETS; MINING ASSOCIATION RULES; PRE-PROCESSING STEP; PREPROCESS;

ASSOCIATION RULES; ASSOCIATIVE PROCESSING; BAYESIAN NETWORKS; CLASSIFIERS; CLUSTER ANALYSIS; DATA MINING; DISTRIBUTED PARAMETER NETWORKS; INFERENCE ENGINES; INTELLIGENT NETWORKS; NEURAL NETWORKS;

LEARNING SYSTEMS;

EID: 70349580621     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2009.05.059     Document Type: Article

References (38)

1. Altman E. Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance 23 (1968) 589-609
2. Altman E.I., Marco G., and Varetto F. Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience). Journal of Banking and Finance 18 (1994) 505-529
3. Baesens B., Verstraeten G., Poel D.V.d., Egmont-Petersen M., Kenhove P.V., and Vanthienen J. Bayesian network classifiers for identifying the slope of the customer - Lifecycle of long-life customers. European Journal of Operational Research 156 (2004) 508-523
4. Castelo, R., Feelders, A. J., & Siebes, A. (2001). MAMBO: Discovering association rules based on conditional independencies. In F. Hoffmann, D. J. Hand, N. M. Adams, D. H. Fisher, & G. Guimarães (Eds.), Proceedings of the fourth international conference on advances in intelligent data analysis (pp. 289-298). Springer-Verlag.
5. Charitou A., Neophytou E., and Charalambous C. Predicting corporate failure: Empirical evidence for the UK. European Accounting Review 13 3 (2004) 465-497
6. Chen M.C., and Huang S.H. Credit scoring and rejected instances reassigning through evolutionary computation techniques. Expert Systems with Applications 24 (2003) 433-441
7. Cheng J., Greiner R., Kelly J., Bell D.A., and Liu W. Learning Bayesian networks from data: An information-theory based approach. The Artificial Intelligence Journal 137 (2002) 43-90
8. Desai V.S., Crook J.N., and Overstreet Jr. G.A. A comparison of neural networks and linear scoring models in the credit union environment. European Journal of Operational Research 95 1 (1996) 24-37
9. Dietterich T.G. An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization. Machine Learning 40 (2000) 139-158
10. Dougherty, J., Kohavi, R., & Sahami, M. (1995). Supervised and unsupervised discretization of continuous features. In A. Prieditis & S. Russell (Eds.), Proceedings of the 12th International conference on machine learning (pp. 194-202). San Francisco, CA: Morgan Kaufmann.
11. Fauré, C., Delprat, S., Boulicaut, J.-F., & Mille, A. (2006). Iterative Bayesian network implementation by using annotated association rules. In Proceedings of the 15th international conference on knowledge engineering and knowledge management (pp. 326-333). Podebrady: Springer.
12. Fayyad, U. M., & Irani, K. B. (1993). Multi-interval discretization of continuous-valued attributes for classification learning. In Proceedings of the 13th international joint conference on artificial intelligence, machine learning (pp. 1022-1027). Chambery, France: Morgan Kaufmann.
13. Hsieh N.C. Hybrid mining approach in the design of credit scoring models. Expert Systems with Applications 28 (2005) 655-665
14. Huang C.L., Chen M.C., and Wang C.J. Credit scoring with a data mining approach based on support vector machines. Expert Systems with Applications 33 (2007) 847-856
15. Islam M.M., Yao X., and Murase K. A constructive algorithm for training cooperative neural network ensembles. IEEE Transactions on Neural Networks 14 (2003) 820-834
16. Jaroszewicz, S., & Simovici, D. A. (2004). Interestingness of frequent itemsets using Bayesian networks as background knowledge. In Proceedings of the 10th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 178-186). Seattle, Washington, USA: ACM.
17. Kerber, R. (1992). Chimerge: Discretization of numeric attributes. In Proceedings of the 10th national conference on artificial intelligence (pp. 123-128). Cambridge, MA: MIT Press.
18. Kim Y.S., and Sohn S.Y. Managing loan customers using misclassification patterns of credit scoring model. Expert Systems with Applications 26 (2004) 567-573
19. Kittler J., Hatef M., Duin R.W., and Matas J. On combining classifiers. IEEE Transactions on Pattern Analysis and Machine Intelligence 20 (1998) 226-239
20. Lawrence E., and Arshadi N. A multinomial logit analysis of problem loan resolution choices in banking. Journal of Money, Credit and Banking 27 (1995) 202-216
21. McKee, T. E. (1998). A mathematically derived rough set model for bankruptcy prediction. In C. E. Brown (Ed.), Seventh annual research workshop on artificial intelligence and emerging technologies in accounting, auditing and tax, artificial intelligence/emerging technologies section of the American accounting association.
22. McKee T.E. Rough sets bankruptcy prediction models versus auditor signaling rates. Journal of Forecasting 22 (2003) 569-586
23. Nilson N.J. Learning machines: Foundations of trainable pattern classifiers (1965), McGraw-Hill, New York
24. Oza N.C., and Tumer K. Classifier ensembles: Select real-world applications. Information Fusion 9 (2008) 4-20
25. Punj G., and Steward D.W. Cluster analysis in marketing research: Review and suggestions for applications. Journal of Marketing Research 20 (1983) 134-148
26. Salchenberger L.M., Cianr E.M., and Lash N.A. Neural networks: A new tool for predicting thrift failures. Decision Sciences 23 (1992) 899-916
27. Sarkar S., and Sriram R.S. Bayesian models for early warning of bank failures. Management Science 47 11 (2001) 1457-1475
28. Schwarz G. Estimating the dimension of a model. The Annals of Statistics 6 (1978) 461-464
29. Sun L., and Shenoy P.P. Using Bayesian networks for bankruptcy prediction: Some methodological issues. European Journal of Operational Research 180 (2007) 738-753
30. Susmaga R. Analyzing discretizations of continuous attributes given a monotonic discrimination function. Intelligent Data Analysis - An International Journal 1 3 (1997) 157-179
31. Swales G., and Yoon Y. Applying artificial neural networks to investment analysis. Financial Analysis Journal 48 (1992) 78-80
32. Tam K.Y., and Kiang M.Y. Managerial applications of neural networks: The case of bank failure predictions. Management Science 38 7 (1992) 926-947
33. Thomas L.C. A survey of credit and behavioural scoring: Forecasting financial risk of lending to consumers. International Journal of Forecasting 16 (2000) 149-172
34. Tsai C.F., and Wu J.W. Using neural network ensembles for bankruptcy prediction and credit scoring. Expert Systems with Applications 34 (2008) 2639-2649
35. Vapnik V. The nature of statistical learning theory. (Chapter 5) (1995), Springer-Verlag, New York
36. West D. Neural network credit scoring models. Computers and Operations Research 27 (2000) 1131-1152
37. West D., Dellana S., and Qian J. Neural network ensemble strategies for financial decision applications. Computers and Operations Research 32 (2005) 2543-2559
38. Zhou Z.H., Jiang Y., Yang Y.B., and Chen S.F. Lung cancer cell identification based on artificial neural network ensembles. Artificial Intelligence in Medicine 24 (2002) 25-36