Abstract
Classification is a key part of credit analysis and bankruptcy prediction and new powerful classification methods coming from artificial intelligence are often applied. Most often classification methods require pre-processing of data. This paper presents a two-part classification process that combines a pre-processing step that uses fuzzy robust principal component analysis (FRPCA) and a classification step. Combinations of three FRPCA algorithms and two different classifiers, similarity classifier and fuzzy k-nearest neighbor classifier, are tested to find the combination that gives the most accurate mean classification result. Tests are run with a small Australian credit data set that can be considered “rough” and to require “robust” methods, due to the small number of observations. The created principal components are used as inputs in the classification methods. Results obtained indicate a mean classification accuracies of over 80 % for all combinations. It becomes clear that parameters of the used methods clearly affect the results and emphasis is put on finding suitable parameters.
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References
Ahn, B., Cho, S., Kim, C.: The integrated methodology of rough set theory and artificial neural network for business failure prediction. Expert Syst. Appl. 18, 65–74 (2000)
Altman, E.I.: Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. J. Finance 23, 589–609 (1968)
Back, B., Laitinen, T., Sere, K.: Neural networks and genetic algorithms for bankruptcy predictions. Expert Syst. Appl. 11, 407–413 (1996)
Beaver, W.H.: Market prices, financial ratios, and the prediction of failure. J. Account. Res. 6, 179–192 (1968)
Dimitras, A.I., Zanakis, S.H., Zopounidis, C.: A survey of business failures with an emphasis on prediction methods and industrial applications. Eur. J. Oper. Res. 90, 487–513 (1996)
Huang, Z., Chen, H., Hsu, C.J., Chen, W.H., Wu, S.: Credit rating analysis with support vector machines and neural networks: a market comparative study. Decis. Support Syst. 37, 543–558 (2004)
Karas, M., Reznakova, M.: To what degree is the accuracy of a bankruptcy prediction model affected by the environment? the case of the Baltic States and the Czech Republic. In: Gimžauskienė, E. (ed.) 19th International Scientific Conference Economics and Management 2014 ICEM-2014, vol. 156, pp. 564–568. Elsevier, Riga, Latvia (2014)
Keller, J., Gray, M., Givens, J.R.: A fuzzy K nearest neighbor algorithm. IEEE Trans. Syst. Man Cybern. 15, 580–585 (1985)
Kumar, P., Ravi, V.: Bankruptcy prediction in banks and firms via statistical and intelligent techniques—a review. Eur. J. Oper. Res. 180, 1–28 (2007)
Lensberg, T., Ellifsen, A., McKee, T.E.: Bankruptcy theory development and classification via genetic programming. Eur. J. Oper. Res. 169, 677–697 (2006)
López Iturriaga, F.J., Pastor Sanz, I.: Bankruptcy visualization and prediction using neural networks: a study of U.S. commercial banks. Expert Syst. Appl. 42, 2857–2869 (2015)
Luukka, P., Leppälampi, T.: Similarity classifier with generalized mean applied to medical data. Comput. Biol. Med. 36, 1026–1040 (2006)
Luukka, P., Saastamoinen, K., Könönen, V.: A classifier based on the maximal fuzzy similarity in the generalized Lukasiewicz-structure. In: FUZZ-IEEE (ed.), pp. 195–198. IEEE, Melbourne, Australia (2001)
Mckee, T.: Developing a bankruptcy prediction model via rough sets theory. Int. J. Intell. Syst. Account. Finance Manage. 16, 159–173 (2000)
Min, J.H., Lee, Y.C.: Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters. Expert Syst. Appl. 28, 603–614 (2005)
Quek, C., Zhou, D., Lee, C.: A novel fuzzy neural approach to data reconstruction and failure prediction. Intell. Syst. Account. Finance. Manage. 16, 165–187 (2009)
Shin, K.S., Lee, T.S., Kim, H.J.: An application of support vector machines in bankruptcy prediction model. Expert Syst. Appl. 28, 127–135 (2005)
Terceno, A., Vigier, H.: Economic-financial forecasting model of businesses using fuzzy relations. Econ. Comput. Econ. Cybern. Stud. Res. 2011, 185–203 (2011)
Yang, T.N., Wang, S.D.: Robust algorithms for principal component analysis. Pattern Recogn. Lett. 20, 927–933 (1999)
Zhang, G., Hu, M.Y., Patuwo, B.E., Indro, D.C.: Artificial neural networks in bankruptcy prediction: general framework and cross-validation analysis. Eur. J. Oper. Res. 116, 16–32 (1999)
Zopounidis, C., Slowinski, R., Doumpus, M., Dimitras, A., Susmaga, R.: Business failure prediction using rough sets: a comparison with multivariate analysis techniques. Fuzzy Econ. Rev. 4, 3–33 (1999)
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Kurama, O., Luukka, P., Collan, M. (2015). Credit Analysis Using a Combination of Fuzzy Robust PCA and a Classification Algorithm. In: Gil-Aluja, J., Terceño-Gómez, A., Ferrer-Comalat, J., Merigó-Lindahl, J., Linares-Mustarós, S. (eds) Scientific Methods for the Treatment of Uncertainty in Social Sciences. Advances in Intelligent Systems and Computing, vol 377. Springer, Cham. https://doi.org/10.1007/978-3-319-19704-3_2
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DOI: https://doi.org/10.1007/978-3-319-19704-3_2
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