Abstract
This paper presents an analysis of credit rating using fuzzy rule-based systems. The disadvantage of the models used in previous studies is that it is difficult to extract understandable knowledge from them. The root of this problem is the use of natural language that is typical for the credit rating process. This problem can be solved using fuzzy logic, which enables users to model the meaning of natural language words. Therefore, the fuzzy rule-based system adapted by a feed-forward neural network is designed to classify US companies (divided into the finance, manufacturing, mining, retail trade, services, and transportation industries) and municipalities into the credit rating classes obtained from rating agencies. Features are selected using a filter combined with a genetic algorithm as a search method. The resulting subsets of features confirm the assumption that the rating process is industry-specific (i.e. specific determinants are used for each industry). The results show that the credit rating classes assigned to bond issuers can be classified with high classification accuracy using low numbers of features, membership functions, and if-then rules. The comparison of selected fuzzy rule-based classifiers indicates that it is possible to increase classification performance by using different classifiers for individual industries.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Ammar S, Duncombe W, Hou Y, Jump B, Wright R (2001) Using fuzzy rule-based systems to evaluate overall financial performance of governments. Public Budgeting Finance 21(4): 91–110
Brabazon A, O’Neill M (2006) Credit classification using grammatical evolution. Informatica 30: 325–335
Brennan D, Brabazon A (2004) Corporate bond rating using neural networks. In: Arabnia H (ed) Proceedings of the Conference on Artificial Intelligence, Las Vegas, pp 161–167
Delahunty A, O’Callaghan D (2004) Artificial immune systems for the prediction of corporate failure and classification of corporate bond ratings. University College Dublin, Dublin
Deshpande A, Iyer SK (2009) The credit risk+ model with general sector correlations. Cent Eur J Oper Res 17(2): 219–228
Hajek P, Olej V (2008) Municipal creditworthiness modelling by Kohonen’s self-organizing feature maps and fuzzy logic neural networks. In: Kurkova V, Neruda R, Koutnik J (eds) Proceedings of the 18th International Conference on Artificial Neural Networks, Prague, pp 533–542
Hajek P (2010) Credit rating modelling by neural networks. Nova Science, New York
Hajek P (2011) Municipal credit rating modelling by neural networks. Decis Support Syst 51(1): 108–118
Hajek P, Olej V (2011) Credit rating modelling by kernel-based approaches with supervised and semi-supervised learning. Neural Comput Appl (in press)
Hall MA (1998) Correlation-based feature subset selection for machine learning. Universtiy of Waikato, Hamilton
Höppner F, Klawonn F, Kruse R, Runkler T (1999) Fuzzy cluster analysis. Wiley, Chichester
Huang Ch, Ruan D (2000) Fuzzy sets and fuzzy information granulation theory (key selected papers by Lotfi A Zadeh). Beijing Normal University Press, Beijing
Huang Z, Chen H, Hsu ChJ, Chen WH, Wu S (2004) Credit rating analysis with support vector machines and neural networks: a market comparative study. Decis Support Syst 37(4): 543–558
Hudec M, Vujosevic M (2010) A fuzzy system for municipalities classification. Cent Eur J Oper Res 18(2): 171–180
Iliadis LS, Skopianos S, Tachos S, Spartalis S (2010) A fuzzy inference system using Gaussian distribution curves for forest fire risk estimation. In: Papadopoulos H, Andreou AS, Bramer M (eds) Proceedings of the 6th Artificial Intelligence Applications and Innovations, Larnaca, pp 376–386
Ishibuchi H, Nozaki K, Tanaka H (1992) Distributed representation of fuzzy rules and its application to pattern classification. Fuzzy Sets Syst 52(1): 21–32
Ishibuchi H, Nakashima T, Morisawa T (1997) Simple fuzzy rule-based classification systems perform well on commonly used real-world data sets. In: Proceedings of the 16th Annual Meeting of the North American Fuzzy Information Processing Society, pp 251–256
Ishibuchi H, Nakashima T, Murata T (1999) Performance evaluation of fuzzy classifier systems for multidimensional pattern classification problems. IEEE Trans Syst Man Cybern Part B Cybern 29(5): 601–618
Jain R, Abraham A (2004) A comparative study of fuzzy classification methods on breast cancer data. Aust Phys Eng Sci Med 27(4): 213–218
Jiao Y, Syau YR, Lee ES (2007) Modelling credit rating by fuzzy adaptive network. Math Comput Model 45(5-6): 717–731
Kim SK (2005) Predicting bond ratings using publicly available information. Expert Syst Appl 29(1): 75–81
Kohavi R, John GH (1997) Wrappers for feature subset selection. Artif Intell 97(1-2): 273–324
Kuncheva LI (2000) Fuzzy classifier design. Springer, Heidelberg
Lee YCh (2007) Application of support vector machines to corporate credit rating prediction. Expert Syst Appl 33(1): 67–74
Lehotsky M, Olej V, Chmurny J (1995) Pattern recognition based on the fuzzy neural networks and their learning by modified genetic algorithms. Neural Netw World 5(1): 91–97
Malhotra R, Malhotra DK (2002) Differentiating between good credits and bad credits using neuro-fuzzy systems. Eur J Oper Res 136(1): 190–211
Michalak K, Kwasnicka H (2010) Correlation based feature selection method. Int J Bio-Inspired Comput 2(5): 319–332
Mitra S, Konwar KM, Pal SK (2002) Fuzzy decision tree, linguistic rules and fuzzy knowledge-based network: generation and evaluation. IEEE Trans Syst Man Cybern 32(4): 328–339
Nauck D, Kruse R (1997) A neuro-fuzzy method to learn fuzzy classification rules from data. Fuzzy Sets Syst 89(3): 277–288
Pedrycz W, Gomide F (2007) Fuzzy systems engineering. Wiley, New Jersey
Samson SB, Bukspan D, Dubois-Pelerin E (2008) General 2008 corporate criteria: analytical methodology. Standard & Poor’s, New York
Wang LX, Mendel JM (1992) Generating fuzzy rules by learning from examples. IEEE Trans Syst Man Cybern 22(6): 1414–1427
Zadeh LA (1978) Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets Syst 1(1): 3–28
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Hájek, P. Credit rating analysis using adaptive fuzzy rule-based systems: an industry-specific approach. Cent Eur J Oper Res 20, 421–434 (2012). https://doi.org/10.1007/s10100-011-0229-0
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10100-011-0229-0