Enhancement of transparency and accuracy of credit scoring models through genetic fuzzy classifier

Maejo International Journal of Science and Technology

Volumn 4, Issue 1, 2010, Pages 136-158

  Lahsasna, Adel ^a   Ainon, Raja N ^a   Wah, Teh Y ^a  

^a UNIVERSITY OF MALAYA   (Malaysia)

Author keywords

Credit scoring; Fuzzy classifier; Genetic algorithms; Transparency

Indexed keywords

EID: 81455150116     PISSN: 19057873     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (30)

1. D. West, "Neural network credit scoring models", Comput. Operat. Res., 2000, 27, 1131-1152.
2. L. C. Thomas, "A survey of credit and behavioral scoring: Forecasting financial risks of lending to customers", Int. J. Forecast., 2000, 16, 149-172.
3. A. Lahsasna, R. N. Ainon and T. Y. Wah, "Credit scoring models using soft computing methods: A survey", Int. Arab J. Inform. Technol., 2010, 7, 115-123.
4. H. L. Jensen, "Using neural networks for credit scoring", Managerial Finance, 1992, 18, 15-26.
5. V. S. Desai, J. N. Crook and G. A. Overstreet, "A comparison of neural networks and linear scoring models in the credit union environment", Eur. J. Operat. Res., 1996, 95, 24-37.
6. B. Baesens, R. Setiono, C. Mues and J. Vanthienen, "Using neural network rule extraction and decision tables for credit-risk evaluation", Manage. Sci., 2003, 49, 312-329.
7. S. Piramuthu, "Financial credit-risk evaluation with neural and neuro-fuzzy systems", Eur. J. Operat. Res., 1999, 112, 310-321.
8. R. Malhotra and D. K Malhotra, "Differentiating between good credits and bad credits using neuro-fuzzy system", Eur. J. Operat. Res., 2002, 136, 190-211.
9. F. Hoffmann, B. Baesens, J. Martens, F. Fput and J. Vanthienen, "Comparing a genetic fuzzy and a neuro-fuzzy classifier for credit scoring", Int. J. Intell. Syst., 2002, 17, 1067-1083.
10. D. Nauck, U. Nauck and R. Kruse, "Generating classification rules with neuro-fuzzy system NEFCLASS", Proceedings of the Biennial Conference of the North American Fuzzy Information Processing Society, 1996, Berkeley, CA, USA, pp.466-470.
11. F. Herrera, "Genetic fuzzy systems: taxonomy, current research trends and prospects", Evol. Intell., 2008, 1, 27-46.
12. H. Ishibuchi, "Multiobjective genetic fuzzy systems: review and future research directions", Proceedings of the 2007 IEEE International Conference on Fuzzy Systems, 2007, London, UK, pp. 913-918.
13. A. Asuncion and D. J. Newman, "UCI Machine Learning Repository", School of Information and Computer Science, University of California, Irvine, CA, http://www.ics.uci.edu/~mlearn/MLRepository.html. (Retrieved: May 15, 2007).
14. R. Quinlan, "C4.5: Programs for Machine Learning", Morgan Kaufmann, San Fransisco, 1992.
15. A. Konak, D. W. Coit and A. E. Smith, "Multi-objective optimization using genetic algorithms: A tutorial", Reliab. Eng. Syst. Saf., 2006, 91, 992-1007.
16. C. A. C. Coello, "A comprehensive survey of evolutionary-based multi-objective optimization techniques", Knowl. Inform. Syst., 1999, 1, 269-308.
17. D. A. Van Veldhuizen and G. B. Lamont, "Multi-objective evolutionary algorithms: analyzing the state-of-the-art", Evol. Comput., 2000, 8, 125-147.
18. K. Deb, A. Pratap, S. Agarwal and T. Meyarivan, "A fast and elitist multi-objective genetic algorithm: NSGA-II," IEEE Trans. Evol. Comput., 2002, 6, 182-197.
19. N. Srinivas and K. Deb, 'Multi-objective optimization using non-dominated sorting in genetic algorithms, Evol. Comput., 1994, 2, 221-248.
20. K. Deb and T. Goel, "Controlled elitist non-dominated sorting genetic algorithms for better convergence", Proceedings of the 1st International Conference on Evolutionary Multi-Criterion Optimization, 2001, Berlin, Germany, pp. 67-81.
21. S. L. Chiu, "Fuzzy model identification based on cluster estimation", J. Intell. Fuzzy Syst., 1994, 2, 267-278.
22. T. Takagi and M. Sugeno, "Fuzzy identification of systems and its application to modeling and control", IEEE Trans. Syst. Man Cybern., 1985, 15, 116-132.
23. The MathWorks, "Matlab Global Optimization Toolbox 3, User's Guide", The MathWorks Inc., Natick, MA, 2010.
24. E. H. Mamdani, "Applications of fuzzy logic to approximate reasoning using linguistic synthesis", IEEE Trans. Comput., 1977, 26, 1182-1191.
25. J. C. Bezdek, "Pattern Recognition with Fuzzy Objective Function Algoritms", Plenum Press, New York, 1981.
26. H. Ishibuchi, T. Nakashima and T. Murata, "Three-objective genetics-based machine learning for linguistic rule extraction", Inform. Sci., 2001, 136, 109-133.
27. R. Kohavi, "A study of cross-validation and bootstrap for accuracy estimation and model selection", Proceedings of International Joint Conference on Artificial Intelligence, 1995, Montreal, Canada, pp.1137-1143.
28. M. Setnes, R. Babuska, U. Kaymak and H. R. van Nauta Lemke, "Similarity measures in fuzzy rule based simplification", IEEE Trans. Syst. Man Cybern. Part B Cybern., 1998, 28, 376-386.
29. C.-L. Huang, M.-C. Chen and C.-J. Wang, "Credit scoring with a data mining approach based on support vector machines", Expert Syst. Appl., 2007, 33, 847-856.
30. C. S. Ong, J. J. Huang and G. H. Tzeng, "Building credit scoring models using genetic programming", Expert Syst. Appl., 2005, 29, 41-47.