A nature inspired Ying-Yang approach for intelligent decision support in bank solvency analysis

Expert Systems with Applications

Volumn 34, Issue 4, 2008, Pages 2576-2587

  Nguyen, M N ^a   Shi, D ^a   Quek, C ^a  

^a NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

Author keywords

Bank solvency analysis; Bayesian Ying Yang learning; Fuzzy CMAC; Truth value restriction

Indexed keywords

DECISION SUPPORT SYSTEMS; FINANCE; FUZZY SETS; MATHEMATICAL MODELS;

BANK SOLVENCY ANALYSIS; BAYESIAN YING-YANG LEARNING; TRUTH VALUE RESTRICTION;

INTELLIGENT SYSTEMS;

EID: 38649098430     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2007.04.020     Document Type: Article

References (57)

1. Altman E. Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance 13 (1968) 589-609
2. Ang K.K., and Quek C. SPOP: Rough set-based pseudo-outer-product fuzzy rule identification algorithm. Neural Computation 17 1 (2005) 205-243
3. Ang K.K., and Quek C. Stock trading using RSPOP: A novel rough set based neuro-fuzzy approach. IEEE Transaction on Neural Networks 17 5 (2006) 1301-1315
4. Ang K.K., Quek C., and Pasquier M. POPFNN-CRI(S): A psuedo outer product based fuzzy neural network using the compositional rule in reference and a Singleton Fuzzifier. IEEE Transactions on systems, man, and cybernetics, Part B: Cybernetics 33 6 (2003) 838-849
5. Ang K.K., Quek C., and Wahab A. MCMAC-CVT: A novel associative memory based CVT transmission control system. Neural Networks 15 3 (2002) 219-236
6. Boritz J., and Kennedy D. Effectiveness of neural network types for prediction of business failure. Expert System Applications 9 (1995) 504-512
7. Centre for Computational Intelligence, Nanyang Technological University, College of Engineering, Singapore, http://www.c2i.ntu.edu.sg/.
8. Coats P., and Fant L. Recognizing financial distress patterns using a neural network tool. Financial Management 22 (1993) 142-155
9. Cole R., and Gunther J. Separating the likelihood and timing of bank failure. Journal of Banking and Finance 19 6 (1995) 1073-1089
10. Federal Reserve Bank of Chicago, URL- http://www.chicagofed.org.
11. Kerling, M. & Poddig, T., (1994). Klassification von Unternehmen mittels KNN, In: H. Rehkugler and H. G. Zimmermann (Eds.), Neuronale Netze in der Okonomie, Munchen, Germany.
12. Kullback S., and Leibler R.A. On information and sufficiency. Annals of Mathematical Statistics 2 1 (1951) 79-86 March
13. Laird N.M., Dempster A.P., and Rubin D.B. Maximum-likelihood from incomplete data via the EM algorithm. Journal of Royal Statistical Society B39 (1977) 1-38
14. Lane W., Looney S., and Wansley J. An application of the Cox proportional hazards model to bank failure. Journal of Banking and Finance 10 (1986) 511-531
15. Lee H.M., Chen C.M., and Lu Y.F. A self-organizing HCMAC neural-network classifier. IEEE Transactions on neural networks 14 1 (2003) 15-27
16. Lee E.S., and Zhu Q. Fuzzy and Evidence Reasoning (1995), Physica-Verlag
17. Lin C.T., and Lee C.S. Neural Fuzzy Systems: A Neuro-Fuzzy Synergism to Intelligent Systems (1996), Prentice Hall
18. Lin C.J., and Lin C.T. An ART-based fuzzy adaptive learning control network. IEEE Transactions on Systems 5 4 (1997) 477-496
19. Liu F., Quek C., and Ng G.S. A novel generic Hebbian ordering based fuzzy rule reduction approach to Mamdani Neuro-fuzzy System. Neural Computation 19 6 (2007) 1656-1680
20. Martin D. Early warning of bank failure: A logit regression approach. Journal of Banking and Finance 1 3 (1977) 249-276
21. Ng, G. S., Quek, C., & Han, J. (in press). FCMAC-EWS: A bank failure early warning system based on a novel localized pattern learning and semantically associative fuzzy neural network. Expert Systems with Applications, doi:10.1016/j.eswa.2006.10.027.
22. Nguyen, M. N., Shi, D., & Quek C. 2005. Self-organizing gaussian fuzzy CMAC with truth value restriction. Proceedings of IEEE International Conference of Information Technology and Applications (ICITA), Sydney, Australia.
23. Nhut M., Shi D., and Quek C. FCMAC-BYY: Fuzzy CMAC Using Bayesian Ying-Yang Learning. IEEE Transactions in Systems, Man and Cybernetics, Part B 36 5 (2006) 1180-1190
24. Ohlson J. Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research 18 (1997) 317-335
25. Ozawa, J., Hayashi, I. & Wakami, N. (1992). Formulation of CMAC-Fuzzy system, IEEE international Conference on Fuzzy Systems - Fuzzy-IEEE (pp. 1179-1186) San Diego, CA.
26. Pasquier M., Quek C., Tan B.T., and Chee S. Opportunistic planning for a fleet of transportation robots. Journal of Engineering Applications of Artificial Intelligence (EAAI) 14 (2001) 329-340
27. Pasquier M., Quek C., and Toh M. Fuzzylot: A self-organising fuzzy-neural rule-based pilot system for automated vehicles. Neural Networks 14 8 (2001) 1099-1112
28. Quah K.H., and Quek C. FITSK: Online local learning with generic fuzzy input Takagi-Sugeno-Kang fuzzy framework for nonlinear system estimation. IEEE Transaction on Systems, Man and Cybernetics, Part B 36 1 (2006) 166-178
29. Quah K., and Quek C. MCES: A novel Monte Carlo evaluative selection approach for objective feature selections. IEEE Transactions on Neural Networks 18 2 (2007) 431-448
30. Quek C., and Ang K.K. An improved MCMAC with momentum, neighbourhood and averaged trapezoidal output. IEEE Transactions on Systems, man and Cybernetics, Part B 30 3 (2000) 491-500
31. Quek C., Pasquier M., and Lim B. POP-TRAFFIC: A novel fuzzy neural approach to road traffic analysis and prediction. IEEE Transactions on Intelligent Transportation Systems 7 2 (2006) 133-146
32. Quek C., and Singh A. POP-Yager: A novel self-organising fuzzy neural network based on the Yager inference. Expert Systems with Applications 29 1 (2005) 229-242
33. Quek C., Tan K.B., and Sagar V.J. Pseudo-outer product based fuzzy neural network fingerprint verification system. Neural Networks 14 3 (2001) 305-323
34. Quek C., and Tung W.L. A novel approach to the derivation of fuzzy membership functions using the falcon-MART architecture. Pattern Recognition Letter 22 9 (2000) 941-958
35. Quek C., and Zhou R.W. The POP learning algorithms: Reducing work in identifying fuzzy rules. Neural networks, the official journal of the international neural network society, European neural network society and Japanese neural network society 14 10 (2001) 1431-1445
36. Quek C., and Zhou R.W. Antiforgery : A novel pseudo-outer product based fuzzy neural network driven signature verification system. Pattern Recognition Letters 23 14 (2002) 1795-1816
37. Quek C., and Zhou R.W. Structure and learning algorithms of a nonsingleton input fuzzy neural network based on the approximate analogical reasoning schema. Fuzzy Sets and Systems 157 13 (2006) 1814-1831
38. Salchenberger L., Cinar E., and Lash N. Neural networks: A new tool for predicting thrift failures. Decision Sciences 23 (1992) 899-916
39. Sim J., Tung WL., and Quek C. FCMAC-Yager: A Novel Yager inference scheme based fuzzy CMAC. IEEE Transactions on Neural Networks 17 6 (2006) 1394-1410
40. Sinkey Jr. J. A multivariate statistical analysis of the characteristics of problem banks. Journal of Finance 1 (1975) 21-36
41. Tam K. Neural network models and the prediction of bank bankruptcy. Omega 19 (1991) 429-445
42. Tam K., and Kiang M. Managerial applications of the neural networks: The case of bank failure predictions. Management Science 38 (1992) 416-430
43. Tang A.M., Quek C., and Ng G.S. GA-TSKFNN: Parameters tuning of fuzzy neural network using genetic algorithms. Expert Systems with Applications 29 4 (2005) 769-781
44. Tan T.Z., Quek C., and Ng G.S. Ovarian cancer diagnosis by hippocampus and neocortex-inspired learning memory structures. Neural Networks 18 5/6 (2005) 818-825
45. Tung W.L., and Quek C. GenSoFNN: A generic self-organizing fuzzy neural network. IEEE Transactions on Neural Networks 13 5 (2002) September
46. Tung W.L., and Quek C. Falcon: Fuzzy neural control and decision systems using FKP and PFKP clustering algorithms. IEEE Transactions on Systems, Man and Cybernetics, Part B 34 1 (2004) 686-694
47. Tung, W. L., & Quek, C. (2005). GenSo-FDSS: A neural-fuzzy decision support system for pediatric ALL cancer subtype identification using gene expression data. Artificial Intelligence in Medicine, Elsevier Science 31(1), 61-88.
48. Tung W.L., Quek C., and Cheng P. GenSo-EWS: A novel neural-fuzzy based early warning system for predicting bank failures. Neural Networks 17 4 (2004) 567-587
49. Wang D., Quek C., and Ng G.S. Novel self-organizing Takagi-Sugeno-Kang fuzzy neural networks based on ART-like clustering. Neural Processing Letters 20 1 (2004) 39-51
50. Xu L. BYY harmony learning, structural RPCL, and topological self-organizing on mixture models. Neural Networks 15 (2002) 1125-1151
51. Xu L. Advances on BYY harmony learning: information theoretic perspective, generalized projection geometry, and independent factor autodetermination. IEEE Transactions on Neural Networks 15 (2004) 885-902
52. Zadeh, L. A. (1975). Calculus of Fuzz restrictions, fuzzy sets and their applications to cognitive and decision process. New York: Academic, pp. 1-39 (Edition).
53. Zadeh L.A. Fuzzy logic, neural networks and soft computing. Communications for ACM 37 3 (1994) 77-84
54. Zhang G., Hu M., Patuwo B., et al. Artificial neural networks in bankruptcy prediction: General framework and cross-validation analysis. European Journal Operations Research 116 (1999) 16-32
55. Zhang K., and Qian F. Fuzzy CMAC and its application. Proceedings of the 3rd World Congress Vol. 2 (2000) 944-947
56. Zhou, R. W., Quek, C. (1996). DCBAM: A discrete chainable bidirectional associative memory, pattern recognition letters, Netherlands: Elsevier Science, 17(9), 985-999.
57. Zhou R.W., Quek C., and Ng G.S. A novel single pass thinning algorithm and an effective set of performing criteria. Pattern Recognition Letters 16 12 (1995) 1267-1275