Intuitionistic Fuzzy Neural Network: The Case of Credit Scoring Using Text Information

  • Petr HájekEmail author
  • Vladimír Olej
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 517)


Intuitionistic fuzzy inference systems (IFISs) incorporate imprecision in the construction of membership functions present in fuzzy inference systems. In this paper we design intuitionistic fuzzy neural networks to adapt the antecedent and consequent parameters of IFISs. We also propose a mean of maximum defuzzification method for a class of Takagi-Sugeno IFISs and this method is compared with the center of area and basic defuzzification distribution operator. On credit scoring data, we show that the intuitionistic fuzzy neural network trained with gradient descent and Kalman filter algorithms outperforms the traditional ANFIS method.


ANFIS Intuitionistic fuzzy sets Intuitionistic fuzzy inference systems of Takagi-Sugeno type Intuitionistic fuzzy neural networks Defuzzification methods 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Liang, Q., Mendel, J.M.: Interval Type-2 Fuzzy Logic Systems: Theory and Design. IEEE Transactions on Fuzzy Systems 8(5), 535–550 (2000)CrossRefGoogle Scholar
  2. 2.
    Hagras, H., Wagner, C.: Towards the Widespread Use of Type-2 Fuzzy Logic Systems in Real World Applications. IEEE Computational Intelligence Magazine 7(3), 4–24 (2012)CrossRefGoogle Scholar
  3. 3.
    Zarandi, F., et al.: A Type-2 Fuzzy Rules-based Expert System Model for Stock Price Analysis. Expert Systems with Applications 36, 139–154 (2009)CrossRefGoogle Scholar
  4. 4.
    Mendel, J.M.: Interval Type-2 Fuzzy Logic Systems Made Simple. IEEE Transactions on Fuzzy Systems 14(6), 808–821 (2006)CrossRefGoogle Scholar
  5. 5.
    Olej, V., Hájek, P.: IF-Inference systems design for prediction of ozone time series: the case of pardubice micro-region. In: Diamantaras, K., Duch, W., Iliadis, L.S. (eds.) ICANN 2010, Part I. LNCS, vol. 6352, pp. 1–11. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  6. 6.
    Olej, V., Hájek, P.: Comparison of fuzzy operators for if-inference systems of takagi-sugeno type in ozone prediction. In: Iliadis, L., Maglogiannis, I., Papadopoulos, H. (eds.) EANN/AIAI 2011, Part II. IFIP AICT, vol. 364, pp. 92–97. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  7. 7.
    Hájek, P., Olej, V.: Adaptive intuitionistic fuzzy inference systems of takagi-sugeno type for regression problems. In: Iliadis, L., Maglogiannis, I., Papadopoulos, H. (eds.) Artificial Intelligence Applications and Innovations. IFIP AICT, vol. 381, pp. 206–216. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  8. 8.
    Hájek, P., Olej, V.: Defuzzification methods in intuitionistic fuzzy inference systems of takagi-sugeno type. The case of corporate bankruptcy prediction. In: The 11th Int. Conf. on on Fuzzy Systems and Knowledge Discovery (FSKD 2014), Xiamen, China, pp. 240–244 (2014)Google Scholar
  9. 9.
    Shing, J., Jang, R.: ANFIS: Adaptive Network Based Fuzzy Inference System. IEEE Transactions on Systems, Man, and Cybernetics 23(3), 665–685 (1993)CrossRefGoogle Scholar
  10. 10.
    Loganathan, C., Girija, K.V.: Hybrid Learning for Adaptive Neuro Fuzzy Inference System. International Journal of Engineering and Science 2(11), 6–13 (2013)Google Scholar
  11. 11.
    Atanassov, K.T.: Intuitionistic Fuzzy Sets. Fuzzy Sets and Systems 20, 87–96 (1986)MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Atanassov, K.T.: Intuitionistic Fuzzy Sets. Physica-Verlag, Heidelberg (1999)CrossRefzbMATHGoogle Scholar
  13. 13.
    Dubois, D., Prade, H.: Interval-valued fuzzy set, possibility theory and imprecise probability. In: Proc. of the Joint 4th Conf. of the European Society for Fuzzy Logic and Technology, EUSFLAT/ LFA, Barcelona, Spain, pp. 314–319 (2005)Google Scholar
  14. 14.
    Akram, M.S., et al.: Intuitionistic Fuzzy Logic Control for Washing Machines. Indian Journal of Science and Technology 7(5), 654–661 (2014)Google Scholar
  15. 15.
    Castillo, O., et al.: An Intuitionistic Fuzzy System for Time Series Analysis in Plant Monitoring and Diagnosis. Applied Soft Computing 7(4), 1227–1233 (2007)CrossRefGoogle Scholar
  16. 16.
    Hájek, P.: Credit Rating Analysis using Adaptive Fuzzy Rule-Based Systems: An Industry Specific Approach. Central European Journal of Operations Research 20(3), 421–434 (2012)CrossRefzbMATHGoogle Scholar
  17. 17.
    Hájek, P., Olej, V.: Evaluating sentiment in annual reports for financial distress prediction using neural networks and support vector machines. In: Iliadis, L., Papadopoulos, H., Jayne, C. (eds.) EANN 2013, Part II. CCIS, vol. 384, pp. 1–10. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  18. 18.
    Atanassov, K.T.: New operations defined over the intuitionistic fuzzy sets. Fuzzy Sets and Systems 61(2), 137–142 (1994)MathSciNetCrossRefzbMATHGoogle Scholar
  19. 19.
    Szmidt, E., Kacprzyk, J.: Distances between intuitionistic fuzzy sets. Fuzzy Sets and Systems 114(3), 505–518 (2000)MathSciNetCrossRefzbMATHGoogle Scholar
  20. 20.
    Klement, E.P., Mesiar, R., Pap, E.: Triangular Norms. Position Paper I: Basic Analytical and Algebraic Properties. Fuzzy Sets and Systems 143, 5–26 (2004)MathSciNetCrossRefzbMATHGoogle Scholar
  21. 21.
    Barrenechea, E.: Generalized atanassov’s intuitionistic fuzzy index. Construction Method. In: IFSA-EUSFLAT, Lisbon, pp. 478–482 (2009)Google Scholar
  22. 22.
    Deschrijver, G., Cornelis, C., Kerre, E.: On the Representation of Intuitionistic Fuzzy t-norm and t-conorm. IEEE Transactions on Fuzzy Systems 12, 45–61 (2004)CrossRefGoogle Scholar
  23. 23.
    Angelov, P.: Crispification: Defuzzification over Intuitionistic Fuzzy Sets. BUSEFAL 64, 51–55 (1995)Google Scholar
  24. 24.
    Angelov, P.: Multi-Objective Optimisation in Air-Conditioning Systems: Comfort/Discomfort Definition by IF Sets. Notes on Intuitionistic Fuzzy Sets 7(1), 10–23 (2001)MathSciNetzbMATHGoogle Scholar
  25. 25.
    Chiu, S.: Fuzzy Model Identification based on Cluster Estimation. Journal of Intelligence and Fuzzy Systems 2, 267–278 (1994)Google Scholar
  26. 26.
    Loughran, T., McDonald, B.: When is a Liability not a Liability? Textual Analysis, Dictionaries, and 10-Ks. The Journal of Finance 66(1), 35–65 (2011)CrossRefGoogle Scholar
  27. 27.
    Hall, M.A.: Correlation-based Feature Selection for Machine Learning. Doctoral dissertation, The University of Waikato (1999)Google Scholar
  28. 28.
    Bernardo, D., Hagras, H., Tsang, E.: A Genetic Type-2 Fuzzy Logic based System for the Generation of Summarised Linguistic Predictive Models for Financial Applications. Soft Computing 17(12), 2185–2201 (2013)CrossRefGoogle Scholar
  29. 29.
    Huarng, K., Yu, H.K.: A Type-2 Fuzzy Time Series Model for Stock Index Forecasting. Statistical Mechanics and its Applications 353, 445–462 (2005)CrossRefGoogle Scholar
  30. 30.
    Sotirov, S., Atanassov, K.: Intuitionistic fuzzy feed forward neural network. Cybernetics and Information Technologies 9(2), 71–76 (2009)MathSciNetGoogle Scholar
  31. 31.
    Chen, L.H., Tu, C.C.: Time-validating-based Atanassov’s Intuitionistic Fuzzy Decision-making. IEEE Transactions on Fuzzy Systems. doi: 10.1109/TFUZZ.2014.2327989

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Institute of System Engineering and Informatics, Faculty of Economics and AdministrationUniversity of PardubicePardubiceCzech Republic

Personalised recommendations