An ant colony optimisation and Nelder-Mead simplex hybrid algorithm for training neural networks: An application to bankruptcy prediction in banks

International Journal of Information and Decision Sciences

Volumn 5, Issue 2, 2013, Pages 188-203

  Sharma, Nigib ^a   Arun, Nampally ^b   Ravi, Vadlamani ^b  

^a INDIAN INSTITUTE OF TECHNOLOGY   (India)

^b INSTITUTE FOR DEVELOPMENT AND RESEARCH IN BANKING TECHNOLOGY   (India)

Author keywords

Ant colony optimization; Bankruptcy prediction in banks; Multi layer perceptron; Nelder Mead simplex method

Indexed keywords

EID: 84893071328     PISSN: 17567017     EISSN: 17567025     Source Type: Journal    
DOI: 10.1504/IJIDS.2013.053798     Document Type: Article

References (43)

1. Ahn, B.A., Cho, S.S. and Kim, C.Y. (2000) 'The integrated methodology of rough set theory and artificial neural network for business failure prediction', Expert Systems with Applications, Vol. 18, No. 2, pp.65-74.
2. Alam, P., Booth, D., Lee, K. and Thordarson, T. (2000) 'The use of fuzzy clustering algorithm and self-organization neural networks for identifying potentially failing banks: an experimental study', Expert Systems with Applications, Vol. 18, No. 4, pp.185-199.
3. Altman, E. (1968) 'Financial ratios: discriminant analysis and the prediction of corporate bankruptcy', Journal of Finance, Vol. 23, No. 4, pp.589-609.
4. Arun, N. and Ravi, V. (2009) 'An ant colony optimization and Nelder-Mead simplex search hybrid algorithm for unconstrained optimization', International Workshop - MIR Day, Nagpur, India.
5. Atiya, A.F. (2001) 'Bankruptcy prediction for credit risk using neural networks: a survey and new results', IEEE Transactions on Neural Networks, Vol. 12, No. 4, pp.929-935.
6. Becerra, V.M., Galvao, R.K.H. and Abou-Seads, M. (2005) 'Neural and wavelet network models for financial distress classification', Data Mining and Knowledge Discovery, Vol. 11, No. 1, pp.35-55.
7. Bell, T. (1997) 'Neural nets or the logit model :a comparison of each model's ability to predict commercial bank failures', International Journal of Intelligent Systems in Accounting, Finance, and Management, Vol. 6, No. 4, pp.249-264.
8. Bhat, T.R., Venkataramani, D., Ravi, V. and Murty, C.V.S. (2006) 'An improved differential evolution method for efficient parameter estimation in bio filter modeling', Biochemical Engineering Journal, Vol. 28, No. 2, pp.167-176.
9. Blum, C. and Socha, K. (2005) 'Training feed forward neural networks with ant colony optimization: an application to pattern classification', Proceedings of the Fifth international Conference on Hybrid Intelligent Systems (HIS '05), pp.233-238.
10. Chang, H., Chen, Y. and Chen, M. (2007) 'Dynamic parameter design by ant colony optimization and neural networks', Asia-Pacific Journal of Operational Research, Vol. 24, No. 3, pp.333-351.
11. Chauhan, N. and Ravi, V. (2010) 'Differential evolution and threshold accepting hybrid algorithm for unconstrained optimization', International Journal of Bio-Inspired Computation, Vol. 2, Nos. 3-4, pp.169-182.
12. Chauhan, N., Ravi, V. and Chandra, D.K. (2009) 'Differential evolution trained wavelet neural network: application to bankruptcy prediction in banks', Expert Systems with Applications, Vol. 36, No. 4, pp.7659-7665.
13. Cheloua, R. and Siarry, P. (2003) 'Genetic and Nelder-Mead algorithms hybridized for a more accurate global optimization of continuous multi minima functions', European Journal of Operational Research, Vol. 148, No. 2, pp.335-348.
14. Cheloua, R. and Siarry, P. (2005) 'A hybrid method combining continuous Tabu search and Nelder-Mead simplex algorithms for the global optimization of multi minima functions', European Journal of Operational Research, Vol. 161, No. 3, pp.636-654.
15. Dorsey, R.E., Johnson, J.D. and Mayer, W.J. (1994) 'A genetic algorithm for training of feed forward neural networks', Advances in Artificial Intelligence in Economics Finance and Management, Vol. 1, pp.93-111.
16. Dreo, J. and Siarry, P. (2004) 'Continuous interacting ant colony algorithm based on dense heterarchy', Future Generation Computer Systems, Vol. 20, No. 5, pp.841-856.
17. Dreo, J. and Siarry, P. (2005) 'An ant colony algorithm aimed at dynamic continuous optimization', Applied Mathematics and Computation, Vol. 181, No. 1, pp.457-467.
18. Dreo, J. and Siarry, P. (2007) 'Hybrid continuous interacting ant colony aimed at enhanced global optimization', Algorithmic Operations Research, Vol. 2, No. 2, pp.52-64.
19. Ilonen, J., Kamarainen, J.K. and Lampinen, J. (2003) 'Differential evolution training algorithm for feed forward neural networks', Neural Process Letters, Vol. 17, No. 1, pp.93-105.
20. Jo, H., Han, I. and Lee, H. (1997) 'Bankruptcy prediction using case-based reasoning, neural networks and discriminant analysis', Expert Systems with Applications, Vol. 13, No. 2, pp.97-108.
21. Lee, K.C., Han, I. and Kwon, Y. (1996) 'Hybrid neural networks for bankruptcy predictions', Decision Support Systems, Vol. 18, No. 1, pp.63-72.
22. Liu, Y., Wu, M. and Qian, J. (2006) 'Evolving neural networks using the hybrid of ant colony optimization and bp algorithms', in Wang, J. et al. (Eds.): ISNN 2006, pp.714-722, Springer-Verlag Berlin Heidelberg, LNCS 3971, ©.
23. Nelder, J.A. and Mead, R. (1965) 'A simplex method for function optimization', The Computer Journal, Vol. 7, No. 4, pp.308-313.
24. Olmeda, I. and Fernandez, E. (1997) 'Hybrid classifiers for financial multi-criteria decision making: the case of bankruptcy prediction', Computational Economics, Vol. 10, No. 4, pp.317-335.
25. Pantalone, C.C. and Platt, M.B. (1987) 'Predicting bank failure since deregulation', New England Economic Review, pp.37-47, Federal Reserve Bank of Boston.
26. Ravi, P.K. and Ravi, V. (2007) 'Bankruptcy prediction in banks and firms via statistical and intelligent techniques - a review', European Journal of Operational Research, Vol. 180, No. 1, pp.1-28.
27. Ravi, V. and Pramodh, C. (2008) 'Threshold accepting trained principal component neural network and feature subset selection: application to bankruptcy prediction in banks', Applied Soft Computing Journal, Vol. 8, No. 4, pp.1539-1548.
28. Ravi, V. and Zimmermann, H.J. (2001) 'A neural network and fuzzy rule base hybrid for pattern classification', Soft Computing, Vol. 5, No. 2, pp.152-159.
29. Ravi, V. and Zimmermann, H.J. (2003) 'Optimization of neural networks via threshold accepting: a comparison with back-propagation algorithm', Conference on Neuro-Computing and Evolving Intelligence, Auckland.
30. Ravi, V., Murty, B.S.N. and Reddy, P.J. (1997) 'Non-equilibrium simulated annealing-algorithm applied to reliability optimization of complex systems', IEEE Transactions on Reliability, Vol. 1, No. 46, pp.233-239.
31. Ravikumar, V., Ravi, P.K., Ravisrinivas, E.S. and Kasabov, N.K. (2007) 'A semi-online training algorithm for the radial basis function neural networks: applications to bankruptcy prediction in banks', in Ravi, V. (Ed.): Advances in Banking Technology and Management: Impact of ICT and CRM, IGI Global Inc., USA.
32. Salchenberger, L., Mine, C. and Lash, N. (1992) 'Neural networks: a tool for predicting thrift failures', Decision Sciences, Vol. 23, No. 4, pp.899-916.
33. Salhi, S. and Queen, N.M. (2004) 'A hybrid algorithm for identifying global and local minima when optimizing functions with many minima', European Journal of Operational Research, Vol. 155, No. 1, pp.51-67.
34. Sexton, R.S., Alidaee, B., Dorsey, R.E. and Johnson, J.D. (1998a) 'Global optimization for artificial neural networks: a Tabu search application', European Journal of Operational Research, Vol. 106, Nos. 2-3, pp.570-584.
35. Sexton, R.S., Dorsey, R.E. and Johnson, J.D. (1998b) 'Toward global optimization of neural networks: a comparison of the genetic algorithm and back-propagation', Decision Support Systems, Vol. 22, No. 2, pp.171-185.
36. Shao, Y. and Chen, Q. (2008) 'Application ant colony neural network in lithology recognition and prediction: evidence from China', IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application.
37. Socha, K. and Dorigo, M. (2008) 'Ant colony optimization for continuous domains', European Journal of Operational Research, Vol. 185, No. 3, pp.1155-1173.
38. Subramanian, V. and Hung, M.S. (1990) 'A GRG2-based system for training neural networks. Design and computational experience', ORSA Journal on computing, Vol. 5, No. 4, pp.386-394.
39. Tam, K.Y. (1991) 'Neural network models and the prediction of bank bankruptcy', OMEGA, Vol. 19, No. 5, pp.429-445.
40. Tam, K.Y. and Kiang, M. (1992) 'Predicting bank failures: a neural network approach', Decision Sciences, Vol. 23, pp.926-947.
41. Tung, W.L., Quek, C. and Cheng, P. (2004) 'GenSo-EWS: a novel neural-fuzzy based early warning system for predicting bank failures', Neural Networks, Vol. 17, No. 4, pp.567-587.
42. Wang, S. (1995) 'The unpredictability of standard Back-propagation neural networks in classification applications', Management Science, Vol. 41, No. 3, pp.555-559.
43. Wilson, R.L. and Sharda, R. (1994) 'Bankruptcy prediction using neural networks', Decision Support Systems, Vol. 11, No. 5, pp.545-557.