Abstract
The induction based on a tree structure is an appropriate representation of the complex human reasoning process such as a corporate bond rating application. Furthermore, the fuzzy decision tree (FDT) can handle the information about vague and incomplete classification knowledge represented in human linguistic terms. In addition, FDT is more flexible by relaxing the constraint of mutual exclusivity of cases in decision tree. We propose a hybrid approach using FDT and genetic algorithms (GA) enhances the effectiveness of FDT to the problem of corporate bond rating classification. This study utilizes a hybrid approach using GA in an attempt to find an optimal or near optimal hurdle values of membership function in FDT. The results show that the accuracy of the integrated approach proposed for this study increases overall classification accuracy rate significantly. We also show that the FDT approach increases the flexibility of the classification process.
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References
Apolloni, B., Zamponi, G., Zanaboni, A.M.: Learning Fuzzy Decision Trees. Neural Networks 11, 885–895 (1998)
Baran, A., Lakonishok, J., Ofer, A.R.: The Value of General Price Level Adjusted Data to Bond Rating. Journal of Business Finance and Accounting 7, 135–149 (1980)
Belkaoui, A.: Industrial Bond Ratings: A New Look. Financial Management 9, 44–51 (1980)
Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York (1987)
Buta, P.: Mining for Financial Knowledge with CBR. AI EXPERT 9, 34–41 (1994)
Chen, M.S., Wang, S.W.: Fuzzy Clustering Analysis for Optimizing Fuzzy Membership Functions. Fuzzy Sets and Systems 103, 239–254 (1999)
Chiang, I., Hsu, J.Y.: Fuzzy Classification Trees for Data Analysis. Fuzzy Sets and Systems 130, 87–99 (2002)
Chiu, S.: Fuzzy Model Identification Based On Cluster Estimation. J. Intell. Fuzzy Systems. 2, 267–278 (1994)
Davis, L.: Handbook of Genetic Algorithms. Van Nostrand Reinhold, NY (1991)
Colin, A.M.: Genetic Algorithms for Financial Modeling. In: Deboeck, G.J. (ed.) Trading on the Edge, pp. 148–173. John Wiley, New York (1994)
Dutta, S., Shekhar, S.: Bond Rating: A Non-conservative Application of Neural Networks. In: Proceedings of IEEE International Conference on Neural Networks, San Diego, CA (1988)
Fogel, D.B.: Applying Evolutionary Programming to Selected Traveling Salesman Problems. Cybernetics and Systems 24 (1993)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, MA (1989)
Han, I., Jo, H., Shin, K.S.: The Hybrid Systems for Credit Rating. Journal of the Korean Operations Research and Management Science Society 22, 163–173 (1997)
Holland, J.H.: Adaptation in Natural and Artificial Systems. The University of Michigan Press, Ann Arbor (1975)
Jeng, B., Jeng, Y., Liang, T.: FILM: a Fuzzy Inductive Learning Method for Automated Knowledge Acquisition. Decision Support Systems 21, 61–73 (1997)
Kim, K.S., Han, I.: The Clustering-indexing Method for Case-based Reasoning Using Selforganizing Maps and Learning Vector Quantization for Bond Rating Cases. Expert Systems with Applications 12, 147–156 (2001)
Klimasauskas, C.C.: Hybrid Fuzzy Encoding for Improved Backpropagation Performance. Advanced Technology for Developers 1, 13–16 (1992)
Koza, J.: Genetic Programming. The MIT Press, Cambridge (1993)
Maher, J.J., Sen, T.K.: Predicting Bond Ratings Using Neural Networks: A Comparison with Logistic Regression. Intelligent Systems in Accounting, Finance and Management 6, 59–72 (1997)
Moody, J., Utans, J.: Architecture Selection Strategies for Neural Networks Application to Corporate Bond Rating. In: Refenes, A. (ed.) Neural Networks in the Capital Markets, John Wiley, Chichester (1995)
Pinches, G.E., Mingo, K.A.: A Multivariate Analysis of Industrial Bond Ratings. Journal of Finance 28, 1–18 (1973)
Quinlan, J.R.: Induction of Decision Trees. Machine learning 1, 81–106 (1986)
Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, Los Altos (1993)
Shaw, M., Gentry, J.: Inductive Learning for Risk Classification. IEEE Expert, 47–53 (1990)
Shin, K.S., Han, I.A.: Case-based Approach Using Inductive Indexing for Corporate Bond Rating. Decision Support Systems 32, 41–52 (2001)
Shin, K.S., Han, I.: Case-based Reasoning Supported by Genetic Algorithms for Corporate Bond Rating. Expert Systems with Applications 16, 85–95 (1999)
Singleton, J.C., Surkan, A.J.: Bond Rating with Neural Networks. In: Refenes, A. (ed.) Neural Networks in the Capital Markets, John Wiley, Chichester (1995)
Syswerda, G.: Uniform Crossover in Genetic Algorithms. In: Schaffer, J.D. (ed.) Proc. 3rd Int. Conf. Genetic Algorithms, Morgan Kaufmann, San Maeto (1989)
Wong, F., Tan, C.: Hybrid neural, genetic and fuzzy systems. In: Deboeck, G.J. (ed.) Trading on the Edge, pp. 245–247. John Wiley, New York (1994)
Yuan, Y., Shaw, M.J.: Induction of Fuzzy Decision Trees. Fuzzy Sets and Systems 69, 125–139 (1995)
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Shin, Ks., Kim, Hj., Kwon, Sb. (2004). A GA-Based Fuzzy Decision Tree Approach for Corporate Bond Rating. In: Zhang, C., W. Guesgen, H., Yeap, WK. (eds) PRICAI 2004: Trends in Artificial Intelligence. PRICAI 2004. Lecture Notes in Computer Science(), vol 3157. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28633-2_54
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DOI: https://doi.org/10.1007/978-3-540-28633-2_54
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