Soft-hard data fusion using uncertainty balance principle – Evidence from corporate credit risk assessment in commercial banking
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Agarwal, P., & Najal, H.S. (2015). Possibility theory vs possibility theory in fuzzy measure theory, Int. Journal of Engineering Research and Applications 5(5), 37–43.
Babashamsi, P., Golzadfar, A., Yusoff, N.I., Ceylan, H., & Nor, N.G. (2016). Integrated fuzzy analytic hierarchy process and VIKOR method in the prioritization of pavement maintenance activities. Int. J. Pavement Res. Technol., 9(2), 112-120.
Bank for International Settlements (February, 2005a), Working Paper No. 15: Studies on the Validation of Internal Rating Systems, Basel: Basel Committee on Banking Supervision, Available at: www.bis.org.
Bank for International Settlements (July, 2005b), An Explanatory Note on the Basel II IRB Risk Weight Functions, Basel: Basel Committee on Banking Supervision, Available at: www.bis.org.
Bellman, R., & Zadeh, L. (1979). Decision-making in a fuzzy environment. Management science.
Bennett, J.C., Bohoris, G.A., Aspinwall, E.M., & Hall, R.C. (1996). Risk analysis techniques and their application to software development. European Journal of Operational Research, 95(3), 467-475.
Blochwitz, S., Hamerle, A., Hohl, S., Rauhmeier & R., Rösch, D. (2005). Myth and reality of discriminatory power for rating systems. Wilmott Magazine, pp. 2-6.
Brkić, S., Hodžić, M., & Džanić, E. (2017). Fuzzy Logic Model of Soft Data Analysis for Corporate Client Credit Risk Assessment in Commercial Banking. Fifth Scientific Conference with International Participation “Economy of Integration” ICEI 2017, Available at SSRN:
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3079471
Brkić, S., Hodžić, M., & Džanić, E. (2019). Soft Data Modeling via Type 2 Fuzzy Distributions for Corporate Credit Risk Assessment in Commercial Banking, In Avdakovic, S. (Ed) Advanced Technologies, Systems and Applications III (pp. 457-469). Springer, Cham.
Central Bank of Bosnia and Herzegovina (2017). The Financial stability report 2017. Retrieved October 10, 2018, Available at: www.cbbh.ba
Coolen, F.P.A., et al. (2010). Imprecise probability. In: Lovric M. (eds) International Encyclopedia of Statistical Science. Berlin, Heidelberg: Springer.
de Cooman, G. (1996). Possibility theory 1, the measure- and integral-theoretic groundwork. Universiteit Gent, Vakgroep Elektrische Energietechniek.
Dubois, D. (2006). Possibility theory and statistical reasoning. Institut de Recherche en Informatique de Toulouse.
Dubois, D., & Prade, H. (1983). Unfair coins and necessity measures: towards a possibilistic interpretation of histograms. Fuzzy Sets Syst., 10(1), 15–20.
Dubois, D., & Prade, H. (1986). Fuzzy sets and statistical data. Eur. J. Oper. Res., 25(3), 345–356.
Dubois, D., & Prade, H. (1987). The mean value of a fuzzy number. Fuzzy Sets Syst., 24(3), 279–300.
Dubois, D., & Prade, H. (1992). When upper probabilities are possibility measures. Fuzzy Sets Syst., 49(1), 65–74.
Dubois, D., & Prade, H. (2002). Possibility theory probability theory and multiple valued logics: a clarification. Annal. Math. Artif. Intell., 32, 35–66.
Dubois, D., & Prade, H., (1988). Possibility Theory. New York: Plenum.
Dubois, D., Prade, H. & Smets, P. (2001). New semantics for quantitative possibility theory. 2nd International Sympoium on Imprecise Probabilities and Their Applications (pp. 152-161). Ithaca, New York.
Engelmann, B., Hayden, E., & Tasche, D. (2003). Testing for Rating Accuracy, Risk 16, January, 82-86.
Eschenbach, W. (2012). Triangular Fuzzy Numbers and the IPCC. Retrieved October 19, 2018, from https://wattsupwiththat.com/2012/02/07/triangular-fuzzy-numbers-and-the-ipcc/
Feller, W. (1950). An Introduction to Probability Theory and Its Applications. New York: Willey.
Garibaldi, J.M., & John. R.I., (2003). Choosing Membership Functions of Linguistic Terms. Proceedings of the 2003 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2003), (pp. 578-583). St. Louis, USA.
Hair Jr. J., Black W. C., Babin B. J. & Andersen R. E. (2010). Multivariate data analysis, 7th Edition, Prentice Hall.
Hayden E. (2011). Estimation of a Rating Model for Corporate Exposures. In Engelmann, B., & Rauhmeier, R, (eds) The Basel II Risk Paramenters (pp.13-24). London: Springer.
Hayden E., Porath D., (2011). Statistical Methods to Develop Rating Models. In Engelmann, B., & Rauhmeier, R, (eds) The Basel II Risk Paramenters (pp.1-12). London: Springer.
Hodžić, M. (2016a). Fuzzy to Random Uncertainty Alignment. Southeast Europe Journal of Soft Computing, 5(1), 58-66.
Hodzic, M. (2016b). Uncertainty Balance Principle. IUS Periodicals of engineering and natural sciences, 4(2), 17-32.
Hodzic, M. (2018). Soft to Hard Data Transformation Using Uncertainty Balance Principle. In Hadzikadic, M. & Avdakovic, S. (Eds) Advanced Technologies, Systems and Applications II (pp. 785-809). Springer International Publishing.
Hodzic, M. (2019). A Platform for Human-Machine Information Data Fusion, In Avdakovic, S. (Ed) Advanced Technologies, Systems and Applications III (pp. 430-456). Springer, Cham.
Hosmer, D., W., & Lemeshow, S. (2010). Applied Logistic Regrresion, 2nd Ed. NJ, USA. John Wiley & Sons, Inc.
Iancu, I., Mamdani, A. (2012). Type fuzzy logic controller. In Dadios, E. (ed.) Fuzzy logic—controls, concepts, theories and applications (pp. 325-350). InTechOpen.
Jenkins, M.P., et al. (2015). Towards context aware data fusion: modeling and integration of situationally qualified human observations to manage uncertainty in a hard-soft fusion process. Information Fusion, 21(1), 130–144.
Kaufmann, A., & Gupta, M.M. (1985). Introduction to Fuzzy Arithmetic, Theory and Applications. New York: Reinhold, Van Nost.
Kaur, B., Bala, M., & Kumar, M. (2014). Comparitive analysis of fuzzy based wildfire detection techniques. Int. J. Sci. Eng. Res., 5(7), 813-818.
Leon-Garcia, A. (2008). Probability, statistics, and random processes for electrical engineers (3rd ed). Upper Saddle River, NJ: Pearson Prentice Hall - Pearson Education, Inc.
Liu, B. (2012). Why is there a need for uncertainty theory? J. Uncertain. Syst., 6(1), 3–10.
Mauris, G. (2011). Possibility distributions: a unified representation of usual direct-probability-based parameter estimation methods. Int. J. Approx. Reason., 52, 1232–1242.
Narukawa, Y., Torra, V., & Gakuen, T. (2016). Fuzzy measure and probability distributions: distorted probabilities. Retrieved July 20, 2018 from http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.183.2292&rep=rep1&type=pdf
Onuwa, O.B. (2014). Fuzzy expert system for malaria diagnosis. Orient. J. Comput. Sci. Technol., 7 (2), 273–284.
Raufaste, E., & Neves, R.D.S. (1998). Empirical evaluation of possibility theory in human radiological diagnosis. In Prade, H. (ed.) 13th European Conference on Artificial Intelligence. Wiley.
Sanchez, L., Casillas, J., Cord, O., & Jose del Jesus, M. (2002). Some relationships between fuzzy and random set-based classifiers and models. Int. J. Approx. Reason., 29, 175–213.
Şentürk, S. (2010). Fuzzy regression control chart based on a-cut approximation. Int. J. Comput.Intell. Syst., 3(1), 123–140.
Shang, K., & Hossen, Z. (2013). Applying Fuzzy Logic to Risk Assessment and Decision-Making. Casualty Actuarial Society, Canadian Institute of Actuaries, Society of Actuaries, 2, 209-218.
Shapiro, A.F. (2009). Fuzzy random variables. Insur. Math. Econ., 44, 307–314.
van der Helm, R. (2008). Towards a clarification of probability, possibility and plausibility: How semantics could help futures practice to improve. Foresight, 8(3), 17–27.
Yang, M.S., & Liu, M.C. (1998). On possibility analysis of fuzzy data. Fuzzy Sets Syst., 94, 171–183.
Zadeh, L.A. (1965). Fuzzy sets. Inf. Control, 8(3), 338–353.
Zadeh, L.A. (2008). Is there a need for fuzzy logic? Info. Sci., 178(13), 2751–2779.
Zimmermann, H. J. (2001). Fuzzy Set Theory – and Its Applications (4th Edition). New York, Kluwer Academic Publishers.
DOI: http://dx.doi.org/10.21533/pen.v7i3.436
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Copyright (c) 2019 Migdat Hodzic, Sabina Brkić, Enis Džanić
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ISSN: 2303-4521
Digital Object Identifier DOI: 10.21533/pen
This work is licensed under a Creative Commons Attribution 4.0 International License