Corporate Credit Rating using Multiclass Classification Models with order Information
Corporate credit rating prediction using statistical and
artificial intelligence (AI) techniques has been one of the attractive
research topics in the literature. In recent years, multiclass
classification models such as artificial neural network (ANN) or
multiclass support vector machine (MSVM) have become a very
appealing machine learning approaches due to their good
performance. However, most of them have only focused on classifying
samples into nominal categories, thus the unique characteristic of the
credit rating - ordinality - has been seldom considered in their
approaches. This study proposes new types of ANN and MSVM
classifiers, which are named OMANN and OMSVM respectively.
OMANN and OMSVM are designed to extend binary ANN or SVM
classifiers by applying ordinal pairwise partitioning (OPP) strategy.
These models can handle ordinal multiple classes efficiently and
effectively. To validate the usefulness of these two models, we applied
them to the real-world bond rating case. We compared the results of
our models to those of conventional approaches. The experimental
results showed that our proposed models improve classification
accuracy in comparison to typical multiclass classification techniques
with the reduced computation resource.
[1] L. Cao, L. K. Guan, and Z. Jingqing, "Bond rating using support vector
machine," Intell. Data Anal., vol. 10, no. 3, pp. 285-296, 2006.
[2] V. Vapnik, The Nature of Statistical Learning Theory. New York:
Springer-Verlag, 1995.
[3] K. Crammer, and Y. Singer, "On the learnability and design of output
codes for multiclass problems," in Proc. 13th Annu. Conf. Computational
Learning Theory, Palo Alto, CA, 2000, pp. 35-46.
[4] J. C. Platt, N. Cristianini, and J. Shawe-Taylor, "Large margin DAG-s for
multiclass classification," in Advances in Neural Information Processing
Systems, vol. 12, S. A. Solla, T. K. Leen, and K. -R. Muller, Eds.
Cambridge, MA: MIT Press, 2000, pp. 547-553.
[5] C. -W. Hsu, and C. -J. Lin, "A comparison of methods for multiclass
support vector machines," IEEE Trans. Neural Networks, vol. 13, no. 2,
pp. 415-425, 2002.
[6] Z. Shuibo, T. Houjun, H. Zhengzhi, and Z. Haoran, "Solving large-scale
multiclass learning problems via an efficient support vector classifier," J.
Syst. Eng. Electron., vol. 17, no. 4, pp. 910-915, 2006.
[7] A. Navia-Vázquez, "Compact multi-class support vector machine,"
Neurocomputing, vol. 71, nos. 1-3, pp. 400-405, 2007.
[8] E. D. Übeyli, "Multiclass support vector machines for diagnosis of
erythemato-squamous disease," Expert Syst. Appl., vol. 35, no. 4, pp.
1733-1740, 2008.
[9] Z. Huang, H. Chen, C. -J. Hsu, W. -H. Chen, and S. Wu, "Credit rating
analysis with support vector machines and neural networks: A market
comparative study," Decis. Support Syst., vol. 37, no. 4, pp. 543-558,
2004.
[10] W. -H. Chen, and J. -Y. Shih, "A study of Taiwan-s issuer credit rating
systems using support vector machines," Expert Syst. Appl., vol. 30, no. 3,
pp. 427-435, 2006.
[11] Y. -C. Lee, "Application of support vector machines to corporate credit
rating prediction," Expert Syst. Appl., vol. 33, no. 1, pp. 67-74, 2007.
[12] Y. S. Kwon, I. Han, and K. C. Lee, "Ordinal pairwise partitioning (OPP)
approach to neural networks training in bond rating," Intell. Syst. Account.
Finance Manag., vol. 6, no. 1, pp. 23-40, 1997.
[13] H. Ahn, J. J. Ahn, H. W. Byun, and K. J. Oh, "A novel customer scoring
model to encourage the use of mobile value added services," Expert Syst.
Appl., vol. 38, no. 9, pp. 11693-11700, 2011.
[14] Y. -C. Wu, Y. -S. Lee, and J. -C. Yang, "Robust and efficient multiclass
SVM models for phrase pattern recognition," Pattern Recogn., vol. 41,
no. 9, pp. 2874-2889, 2008.
[15] A. C. Lorena, and A. C. P. L. F. de Carvalho, "Investigation of strategies
for the generation of multiclass support vector machines," in New
Challenges in Applied Intelligence Techniques, N. T. Nguyen, and R.
Katarzyniak, Eds. Berlin, Germany: Springer-Verlag, 2008, pp. 319-328.
[16] J. Friedman, "Another approach to polychtomous classification,"
Technical Report, Stanford University, 1996.
[17] U. Kreβel, "Pairwise classification and support vector machines," in
Advances in Kernal Methods: Support Vector Learning, ch. 15, B.
Schölkopf, C. Burges, and A. J. Smola, Eds. Cambridge, MA: MIT Press,
1999, pp. 255-268.
[18] J. Weston, and C. Watkins, "Support vector machines for multiclass
pattern recognition," in Proc. 7th European Symp. Artificial Neural
Networks, Bruges, Belgium, 1999, pp. 219-224.
[19] C. -W. Hsu, and C. -J. Lin, "A simple decomposition method for support
vector machines," Mach. Learn., vol. 46, nos. 1-3, pp. 291-314, 2002.
[20] F. E. H. Tay, and L. J. Cao, "Application of support vector machines in
financial time series forecasting," Omega, vol. 29, no. 4, pp. 309-317,
2001.
[21] C. -C. Chang, and C. -J. Lin, LIBSVM : a library for support vector
machines, 2001.
Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm/
[1] L. Cao, L. K. Guan, and Z. Jingqing, "Bond rating using support vector
machine," Intell. Data Anal., vol. 10, no. 3, pp. 285-296, 2006.
[2] V. Vapnik, The Nature of Statistical Learning Theory. New York:
Springer-Verlag, 1995.
[3] K. Crammer, and Y. Singer, "On the learnability and design of output
codes for multiclass problems," in Proc. 13th Annu. Conf. Computational
Learning Theory, Palo Alto, CA, 2000, pp. 35-46.
[4] J. C. Platt, N. Cristianini, and J. Shawe-Taylor, "Large margin DAG-s for
multiclass classification," in Advances in Neural Information Processing
Systems, vol. 12, S. A. Solla, T. K. Leen, and K. -R. Muller, Eds.
Cambridge, MA: MIT Press, 2000, pp. 547-553.
[5] C. -W. Hsu, and C. -J. Lin, "A comparison of methods for multiclass
support vector machines," IEEE Trans. Neural Networks, vol. 13, no. 2,
pp. 415-425, 2002.
[6] Z. Shuibo, T. Houjun, H. Zhengzhi, and Z. Haoran, "Solving large-scale
multiclass learning problems via an efficient support vector classifier," J.
Syst. Eng. Electron., vol. 17, no. 4, pp. 910-915, 2006.
[7] A. Navia-Vázquez, "Compact multi-class support vector machine,"
Neurocomputing, vol. 71, nos. 1-3, pp. 400-405, 2007.
[8] E. D. Übeyli, "Multiclass support vector machines for diagnosis of
erythemato-squamous disease," Expert Syst. Appl., vol. 35, no. 4, pp.
1733-1740, 2008.
[9] Z. Huang, H. Chen, C. -J. Hsu, W. -H. Chen, and S. Wu, "Credit rating
analysis with support vector machines and neural networks: A market
comparative study," Decis. Support Syst., vol. 37, no. 4, pp. 543-558,
2004.
[10] W. -H. Chen, and J. -Y. Shih, "A study of Taiwan-s issuer credit rating
systems using support vector machines," Expert Syst. Appl., vol. 30, no. 3,
pp. 427-435, 2006.
[11] Y. -C. Lee, "Application of support vector machines to corporate credit
rating prediction," Expert Syst. Appl., vol. 33, no. 1, pp. 67-74, 2007.
[12] Y. S. Kwon, I. Han, and K. C. Lee, "Ordinal pairwise partitioning (OPP)
approach to neural networks training in bond rating," Intell. Syst. Account.
Finance Manag., vol. 6, no. 1, pp. 23-40, 1997.
[13] H. Ahn, J. J. Ahn, H. W. Byun, and K. J. Oh, "A novel customer scoring
model to encourage the use of mobile value added services," Expert Syst.
Appl., vol. 38, no. 9, pp. 11693-11700, 2011.
[14] Y. -C. Wu, Y. -S. Lee, and J. -C. Yang, "Robust and efficient multiclass
SVM models for phrase pattern recognition," Pattern Recogn., vol. 41,
no. 9, pp. 2874-2889, 2008.
[15] A. C. Lorena, and A. C. P. L. F. de Carvalho, "Investigation of strategies
for the generation of multiclass support vector machines," in New
Challenges in Applied Intelligence Techniques, N. T. Nguyen, and R.
Katarzyniak, Eds. Berlin, Germany: Springer-Verlag, 2008, pp. 319-328.
[16] J. Friedman, "Another approach to polychtomous classification,"
Technical Report, Stanford University, 1996.
[17] U. Kreβel, "Pairwise classification and support vector machines," in
Advances in Kernal Methods: Support Vector Learning, ch. 15, B.
Schölkopf, C. Burges, and A. J. Smola, Eds. Cambridge, MA: MIT Press,
1999, pp. 255-268.
[18] J. Weston, and C. Watkins, "Support vector machines for multiclass
pattern recognition," in Proc. 7th European Symp. Artificial Neural
Networks, Bruges, Belgium, 1999, pp. 219-224.
[19] C. -W. Hsu, and C. -J. Lin, "A simple decomposition method for support
vector machines," Mach. Learn., vol. 46, nos. 1-3, pp. 291-314, 2002.
[20] F. E. H. Tay, and L. J. Cao, "Application of support vector machines in
financial time series forecasting," Omega, vol. 29, no. 4, pp. 309-317,
2001.
[21] C. -C. Chang, and C. -J. Lin, LIBSVM : a library for support vector
machines, 2001.
Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm/
@article{"International Journal of Business, Human and Social Sciences:52681", author = "Hyunchul Ahn and Kyoung-Jae Kim", title = "Corporate Credit Rating using Multiclass Classification Models with order Information", abstract = "Corporate credit rating prediction using statistical and
artificial intelligence (AI) techniques has been one of the attractive
research topics in the literature. In recent years, multiclass
classification models such as artificial neural network (ANN) or
multiclass support vector machine (MSVM) have become a very
appealing machine learning approaches due to their good
performance. However, most of them have only focused on classifying
samples into nominal categories, thus the unique characteristic of the
credit rating - ordinality - has been seldom considered in their
approaches. This study proposes new types of ANN and MSVM
classifiers, which are named OMANN and OMSVM respectively.
OMANN and OMSVM are designed to extend binary ANN or SVM
classifiers by applying ordinal pairwise partitioning (OPP) strategy.
These models can handle ordinal multiple classes efficiently and
effectively. To validate the usefulness of these two models, we applied
them to the real-world bond rating case. We compared the results of
our models to those of conventional approaches. The experimental
results showed that our proposed models improve classification
accuracy in comparison to typical multiclass classification techniques
with the reduced computation resource.", keywords = "Artificial neural network, Corporate credit rating,
Support vector machines, Ordinal pairwise partitioning", volume = "5", number = "12", pages = "1825-6", }
