Abstract
We propose the particle swarm optimization (PSO) trained auto associative neural network (AANN) as a single class classifier (PSOAANN). The proposed architecture consists of three layers namely input layer, hidden layer and output layer unlike that of the traditional AANN. The efficacy of the proposed single class classifier is evaluated on bankruptcy prediction datasets namely Spanish banks, Turkish banks, US banks and UK banks; UK credit dataset and the benchmark WBC dataset. PSOAANN achieved better results when compared to Modified Great Deluge Algorithm trained auto associative neural network (MGDAAANN) [1]. It is concluded that PSOAANN as a single class classifier can be used as an effective tool in classifying datasets, where the class of interest (usually the positive class) is either totally missing or disproportionately present in the training data, which is the case in many real life problems for e.g. financial fraud detection.
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References
Pramod, C., Ravi, V.: Modified Great Deluge Algorithm based Auto-Associative Neural Network for bankruptcy Prediction. International Journal of Computational Intelligence Research 3(4), 363–370 (2007)
Baek, J., Cho, S.: Bankruptcy Prediction for credit Risk Using an Auto-Associative Neural Network in Korean Firms. In: The Proceedings of the CIFEr, Hong Kong (2003)
Altman, E.: Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy. Journal of Finance 23, 589–609 (1968)
Wilson, R.L., Sharda, R.: Bankruptcy prediction using neural networks. Decision Support Systems 11, 545–557 (1994)
Cole, R., Gunther, J.: A CAMEL Rating’s Shelf Life. Federal Reserve Bank of Dallas Review, 13–20 (December 1995)
Fraser, D.: The Determinants of Bank Profits: An Analysis of Extremes. Financial Review 11, 69–87 (1976)
Karels, G.V., Prakash, A.J.: Multivariate normality and forecasting for business bankruptcy. Journal of Business Finance & Accounting 14, 573–593 (1987)
Odom, M., Sharda, R.: A Neural Network for Bankruptcy Prediction. In: Proceedings of the IJCNN International Conference on Neural Networks, San Diego, CA (1990)
Ohlson, J.A.: Financial Rations and the Probabilistic Prediction of Bankruptcy. Journal of Accounting Research 18, 109–131 (1980)
Tam, K.Y.: Neural Network Models and the Prediction of Bank Bankruptcy. OMEGA 19, 429–445 (1991)
Salchenberger, L., Mine, C., Lash, N.: Neural Networks: A Tool for Predicting Thrift Failures. Decision Sciences 23, 899–916 (1992)
Tam, K.Y., Kiang, M.: Predicting Bank Failures: A Neural Network Approach. Decision Sciences 23, 926–947 (1992)
Atiya, A.F.: Bankruptcy prediction for credit risk using neural networks: A survey and new results. The IEEE Transactions on Neural Networks 12, 929–935 (2001)
Shin, K.S., Lee, T.S., Kim, H.J.: An application of support vector machines in bankruptcy prediction model. Expert Systems with Applications (28), 127–135 (2005)
Canbas, S., Caubak, Kilic, S.B.: Prediction of commercial bank failure via multivariate statistical analysis of financial structures: The Turkish case. European Journal of Operational Research 166, 528–546 (2005)
Ravikumar, P., Ravi, V.: Bankruptcy prediction in banks by Fuzzy Rule based classifier. In: The Proceedings of 1st IEEE International Conference on Digital and Information Management, Bangalore (2006)
Ravi, V., Ravi Kumar, P., Ravi Srinivas, E., Kasabov, N.K.: 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. Idea Group Inc., USA (2007)
Ravikumar, P., Ravi, V.: Bankruptcy prediction in Banks by an Ensemble classifier. In: The Proceedings of IEEE International Conference on Industrial Technology, Mumbai (2006)
Ravi Kumar, P., Ravi, V.: Bankruptcy prediction in banks and firms via statistical and intelligent techniques - A Review. Eur. J. Oper. Res. (2006), doi:10.1016/j.ejor.2006.08.043
Ravi, V., Kurniawan, H., Thai, P.N.K., Ravikumar, P.: Soft Computing System for Bank Performance prediction. Accepted in Applied Soft Computing Journal (2007)
Farquad, M.A.H., Ravi, V., Sriramjee, Praveen, G.: Credit Scoring Using PCA-SVM Hybrid Model. In: Das, V.V., Stephen, J., Chaba, Y. (eds.) CNC 2011. CCIS, vol. 142, pp. 249–253. Springer, Heidelberg (2011)
Kennedy, J., Eberhart, R.C.: Particle Swarm Optimization. In: Proceeding of IEEE International Conference on Neural Networks, Piscataway, NJ, USA, pp. 1942–1948 (1995)
Olmeda, I., Fernandez, E.: Hybrid classifiers for financial multicriteria decision making: The case of bankruptcy prediction. Comp. Economics 10, 317–335 (1997)
Canbas, S., Caubak, B., Kilic, S.B.: Prediction of commercial bank failure via multivariate statistical analysis of financial structures: The Turkish case. European Journal of Operational Research 166, 528–546 (2005)
Beynon, M.J., Peel, M.J.: Variable Precision Refought Set Theory and Data Discretisation: An Application to Corporate Failure Prediction. Omega 29, 561–576 (2001)
Rahimian, E., Singh, S., Thammachote, T., Virmani, R.: Bankruptcy prediction by neural network. In: Trippi, R.R., Turban, E. (eds.) Neural Networks in Finance and Investing, Burr Ridge, Irwin Professional Publishing, USA (1996)
Thomas, L.C., Edelman, D.B., Crook, J.N.: Credit scoring and its applications. SIAM, Philadelphia (2002)
Asuncion, A., Newman, D.J.: UCI machine learning repository. University of California, School of Information and Computer Science, Irvine, CA (2007)
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Ravi, V., Nekuri, N., Das, M. (2012). Particle Swarm Optimization Trained Auto Associative Neural Networks Used as Single Class Classifier. In: Panigrahi, B.K., Das, S., Suganthan, P.N., Nanda, P.K. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2012. Lecture Notes in Computer Science, vol 7677. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35380-2_67
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DOI: https://doi.org/10.1007/978-3-642-35380-2_67
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