Browse > Article

GA-based Normalization Approach in Back-propagation Neural Network for Bankruptcy Prediction Modeling  

Tai, Qiu-Yue (College of Business Administration, Ewha Womans University)
Shin, Kyung-Shik (College of Business Administration, Ewha Womans University)
Publication Information
Journal of Intelligence and Information Systems / v.16, no.3, 2010 , pp. 1-14 More about this Journal
Abstract
The back-propagation neural network (BPN) has long been successfully applied in bankruptcy prediction problems. Despite its wide application, some major issues must be considered before its use, such as the network topology, learning parameters and normalization methods for the input and output vectors. Previous studies on bankruptcy prediction with BPN have shown that many researchers are interested in how to optimize the network topology and learning parameters to improve the prediction performance. In many cases, however, the benefits of data normalization are often overlooked. In this study, a genetic algorithm (GA)-based normalization transform, which is defined as a linearly weighted combination of several different normalization transforms, will be proposed. GA is used to extract the optimal weight for the generalization. From the results of an experiment, the proposed method was evaluated and compared with other methods to demonstrate the advantage of the proposed method.
Keywords
정규화 기법;역전파 알고리즘;유전자알고리즘;부도예측모형;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 Visalakshi, N. and Thagavel, K., "Impact of normalization in distributed k-means clustering", International Journal of Soft Computing, Vol.4, No.4(2009), 168-172.
2 Swicegood, P., and Clark, J., "Off-site monitoring for predicting bank under performance : a comparison of neural networks, discriminant analysis and professional human judgement", International Journal in Accounting, Finance and Management, Vol.10(2001), 169-186.
3 Tam K., "Neural networks models and the prediction of bankruptcy", Omega, Vol.19, No.5(1991), 429-445.   DOI   ScienceOn
4 Tsai, C. and Wu, J., "Using neural network ensembles for bankruptcy prediction and credit scoring", Expert Systems with Applications, Vol.34(2008), 2639-2649.   DOI   ScienceOn
5 Sharda, R. and Wilson, R., "Performance comparison issues in neural network experiments for classification problems", Proceedings of the 26th Hawaii International Conference on System Sciences, IEEE Press, 1993.
6 A1 Shalabi, L., and Shaaban, Z., "Normalization as a preprocessing engine for data mining and the approach of preference matrix", IEEE Computer Society, Proceedings of the Internatinal Conference on Dependability of Computer Systems, 0-7695-256, (2006).
7 O'leary, D., "Using neural networks to predict corporate failure", International Journal of Intelligent Systems in Accounting, Finance and Management, Vol.7(1998), 187-197.   DOI   ScienceOn
8 Kim, K. W., Kim, D., and Jung, H. "Normalization methods on back-propagation for the estimation of drivers' route choice", KSCE Journal of Civil Engineering, Vol.9 No.5(2005), 403-406.   DOI   ScienceOn
9 Kim, K. and Han, I., "Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index", Expert Systems with Applications, Vol.19(2000), 125-132.   DOI   ScienceOn
10 Javanmard, H. and Saleh, F., "The comparison artificial neural networks and multi decimal analysis models for forecasting bankruptcy and financial distress", Proceedings of the Worlds Congress on Engineering, July, 2009.
11 Akdemir, B. and Yu, L., "Elliot waves predicting for stock marketing using Euclidean based normalization method merged with artificial neural network", Fourth International Conference on Computer Science and Convergence Information Technology, (2009), 562-567.
12 Olden, J. and Jackson, D., "Illuminating the "black box" : a randomization approach for understanding variable contribution in artificial networks", Ecological Modeling, Vol.154, No.1-2(2002), 135-150.   DOI   ScienceOn
13 Shin, K. and Lee, T., "A genetic algorithm application in bankruptcy prediction modeling", Expert Systems with Applications, Vol.23, No.3(2002), 321-328   DOI   ScienceOn
14 Surkan, A. and Singleton, J., "Neural networks for bond rating improved by multiple hidden layers", International Joint Conference on Neural Networks, (1990), 157-162.
15 Shin, K. and Han, I., "Case-based reasoning supported by genetic algorithms for corporate bond rating", Expert Systems with Applications, Vol.23, No.3(2002), 321-328.   DOI   ScienceOn
16 Sen, T., Ghandforoush, P., and Stivason, C., "Improving Prediction Of Neural Networks : A Study Of Two Financial Prediction tasks", Journal of Applied Mathematics and Decision Science, Vol.8, No.4(2004), 219-233.
17 Shanker, M., Hu, M., and Hung, M., "Effect of data standardization on neural network training", Omega, International Journal of Science, Vol.24 No.4(1996), 385-397.
18 Salchenberger, L., Cinear, E., and Lash, N., "Neural Networks : A new tools for Thrift failure", Decision Science, Vol.23(1992), 899-916.   DOI
19 Schaffer, C. and Green, P., "An empirical comparison of variable standardization methods in cluster analysis", Multivariate Behavioral Research, Vol.31, No.2(1996), 149-167.   DOI   ScienceOn
20 Rahimian, E., Singh, S., and Thammachote, T., "Bankruptcy prediction by neural network", Neural Networks in Finance and Investing : Using Artificial Intelligence to Improve Real-World Performance, Probus, Chicage, IL., (1993), 159-176.
21 Kim, D. "Normalization methods for input and output vectors in back-propagation neural networks", International Journal of Computer Mathematics, Vol.71(1999), 161-171.   DOI
22 Moody, J. and Utans, J., "Architecture selection strategies for neural networks : Application to bond rating prediction", Neural Networks in the Capital Markets, (1995).
23 Min, S., Lee, J., and Han, I., "Hybrid genetic algorithms and support vector machines for bankruptcy prediction", Expert Systems with Applications, Vol.31(2006), 652-660.   DOI   ScienceOn
24 Mitchel, M., "An introduction to genetic algorithms", Cambridge, MA : The MIT Press, 1996.
25 Liu, C. and Marukawa, K., "Normalization ensemble for handwritten character recognition", IEEE Computer Society, International Conference on Computational Intelligence for Modeling Control and Automation, 0-7695-218, (2004).
26 Mazzatorta,P. and Benfenati, E., "The importance of scaling in data mining for toxicity prediction", Journal of Chemical Information and Computer Sciences, Vol.42(2002), 1250-1255.   DOI
27 Kim, M. and Han, I., "The discovery of experts' decision rules from qualitative bankruptcy data using genetic algorithms", Expert Systems with Applications, Vol.25(2003), 637-646.   DOI   ScienceOn
28 Kotsiantis, S., Kanellopoulos, D., and Pintelas, P., "Data preprocessing for supervised learning", Internatinal Journal of Computer Science, Vol.1, No.2(2006), 1306-4428.
29 Leshno, M., "Neural network prediction analysis: The bankruptcy case", Neural computing, Vol.10, No.2(1996), 125-147.
30 Khashman, A., "Neural networks for credit risk evaluation : Investigation of different neural models and learning schemes", Vol.37, No.9 (2010). 6233-6239   DOI
31 Jolai, F. and Ghanbari, A., "Integrating data transformation techniques with Hopfield neural networks for solving travelling salesman problem", Expert Systems with Applications, Vol.37, No.7(2010), 5331-5335.   DOI   ScienceOn
32 Wu, C., Tzeng, G., Goo, Y., and Fang, W., "A real-valued genetic algorithm to optimize the parameters of support vector machine for predicting bankruptcy", Expert Systems with Applications, Vol.32, No.2(2007), 397-408.   DOI   ScienceOn
33 Jiang, Y., Wang, Y., and Xu, L., "Using genetic algorithms to predict financial performance", IEEE Xplore, 1-4244-099, (2007), 3255-3229.
34 Chen, W. and Du, Y., "Using neural networks and data mining techniques for the financial distress prediction model", Expert Systems with Applications, Vol.36(2009), 4075-4086.   DOI   ScienceOn
35 Chia, W., Holliday, J., and Willett, P., "Effect of data standardization on chemical clustering and similarity searching", Journal of Chemical Information and Modeling, Vol.49 No.2(2009), 155-161.
36 Cror, K., and Ross, A., "Score normalization in multimodal biometric systems", Pattern Recognition, Vol.38(2005), 2270-2285.   DOI   ScienceOn
37 Berry, M. and Linoff, G., "Data mining techniques for marketing, sales and customer support", Wiley Computer Publishing, 1997.
38 Barniv, R., Anuragh, A., and Leach R., "Predicting the outcome following bankruptcy filing : a three state classification using NN", International Journal of Intelligence System in Accounting, Finance and Management, Vol.6(1997), 177-194.   DOI   ScienceOn
39 Yim, J. and Mitchell, H., "A comparison of corporate failure models in Australia : Hybrid neural networks, logit models and discriminant analysis", RMIT Working Paper, Vol.10(2002).
40 Wilson, R. and Shandra, R., "Bankruptcy prediction using neural networks", Decision Support Systems, Vol.11, No.5(1994), 545-557.   DOI   ScienceOn
41 Utans, J. and Moody, J., "Selecting neural network architecture via the prediction risk : Application to corporate bond rating prediction", IEEE Xplore, (1994), 35-41.
42 Wang, H. and Zhang, J., "Analysis of different data standardization form for fuzzy clustering evaluation results' influence", IEEE Xplore, 978-1-4244, (2009), 1-4.
43 Tsukuda, J. and Baba, S., "Predicting Japanese corporate bankruptcy in terms of finance data using neural network", Computer and Industrial Engineering, Vol.1, No.4(1994), 445-448.