Abstract
The paper presents FCMCR a fuzzy classification method for credit risk in banking system. Our implementation makes use of fuzzy rules to evaluate similarity between objects as well as using membership degree for features respect to each class. The method is inspired by Fuzzy classification method and was tested using loan data from a large bank. Our result shows that the proposed method is competitive with other approaches reported in the literature.
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References
Yu, L., Lai, K.K., Wang, S., Zhou, L.: A Least Squares Fuzzy SVM Approach to Credit Risk Assessment. In: Fuzzy Information and Engineering (ICFIE), pp. 865–874 (2007)
Piramuthu, S.: Financial credit-risk evaluation with neural and neurofuzzy systems. European Journal of Operational Research (August 1997)
Galindo, J., Tamayo, P.: Credit Risk Assessment Using Statistical and Machine Learning: Basic Methodology and Risk Modelling Applications. Computational Economics 15, 107–143 (2000)
Xu, R., Wunsch, D.: Clustering. IEEE Press Series on Computational Intelligence (2009)
Wu, X., Kumar, V., Ross Quinlan, J., Ghosh, J., Yang, Q., Motoda, H., McLachlan, G.J., Ng, A., Liu, B., Yu, P.S., Zhou, Z.-H., Steinbach, M., Hand, D.J., Steinberg, D.: Top 10 algorithms in data mining. Springer, London (2007)
Alves, A.C.P.D.: Fuzzy Models in Credit Risk Analysis. SCI, pp. 353–367. Springer, Heidelberg (2007)
Yao, Y.: Credit Risk Assessment of Online Shops Based on Fuzzy Consistent Matrix. Applied Mathematics and Information Sciences, pp. 163–169 (2011)
Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann, Elsevier (2006)
Ross Timothy, J.: Fuzzy Logic with Engineering Applications, 2nd edn. Wiley (2004)
Borgelt, C.: Prototype-based Classification and Clustering. Magdeburg (June 22, 2005)
Roubos, J.A., Setnes, M., Abonyi, J.: Learning Fuzzy Classification Rules from Data, pp. 77–93. Elsevier Science Inc. (2002)
Chiu, S.L.: An Efficient Method for Extracting Fuzzy Classification Rules from High Dimensional Data. J. Advanced Computational Intelligence (1997)
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Yazdani, H., Kwasnicka, H. (2012). Fuzzy Classification Method in Credit Risk. In: Nguyen, NT., Hoang, K., Jȩdrzejowicz, P. (eds) Computational Collective Intelligence. Technologies and Applications. ICCCI 2012. Lecture Notes in Computer Science(), vol 7653. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34630-9_51
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DOI: https://doi.org/10.1007/978-3-642-34630-9_51
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34629-3
Online ISBN: 978-3-642-34630-9
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