International Journal of Fuzzy Logic and Intelligent Systems

  • 제6권4호
  • /
  • Pages.331-334
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  • 2006
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  • 1598-2645(pISSN)
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  • 2093-744X(eISSN)
한국지능시스템학회 (Korean Institute of Intelligent Systems)
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Multiclass SVM Model with Order Information

  • Ahn, Hyun-Chul (Graduate School of Management, Korea Advanced Institute of Science & Technology) ;
  • Kim, Kyoung-Jae (Department of MIS, Dongguk University)
  • 발행 : 2006.12.01
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초록

Original Support Vsctor Machines (SVMs) by Vapnik were used for binary classification problems. Some researchers have tried to extend original SVM to multiclass classification. However, their studies have only focused on classifying samples into nominal categories. This study proposes a novel multiclass SVM model in order to handle ordinal multiple classes. Our suggested model may use less classifiers but predict more accurately because it utilizes additional hidden information, the order of the classes. To validate our model, we apply it to the real-world bond rating case. In this study, we compare the results of the model to those of statistical and typical machine learning techniques, and another multi class SVM algorithm. The result shows that proposed model may improve classification performance in comparison to other typical multiclass classification algorithms.

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참고문헌

  1. Crammer, K., Singer, Y.: On the Leamability and Design of Output Codes for Multiclass Problems. Proc. of the 13th Annual Conference on Computational Learning Theory (2000) 35-46
  2. Drucker, H., Wu, D., Vapnik, V.N.: Support vector machines for spam categorization. IEEE Transactions on Neural Networks, 10(5) (1999) 1048-1054 https://doi.org/10.1109/72.788645
  3. Friedman, J.: Another Approach to Polychtomous Classfication. Technical Report, Stanford University (1996)
  4. Hsu, C-W, Lin, C.-J.: A Comparison of Methods for Multiclass Support Vector Machines. IEEE Transactions on Neural Networks 13(2) (2002) 415-425 https://doi.org/10.1109/72.991427
  5. Hsu, C-W., Lin, C.-J.: A Simple Decomposition Method for Support Vector Machines. Machine Learning 46 (2002) 291-314 https://doi.org/10.1023/A:1012427100071
  6. Huang, Z., Chen, H., Hsu, C-J., Chen, W-H., Wu, S.: Credit Rating Analysis with Support Vector Machines and Neural Networks: A Market Comparative Study. Decision Support Systems 27 (2004) 543-558
  7. Kressel, U.: Pairwise Classification and Support Vector Machines. In Scholkopf, B., Burges, C., Smola, A.J.: Advances in Kernal Methods: Support Vector Learning Chapter 15. MIT Press. Cambridge, MA (1999) 255-268
  8. Kwon, Y.S., Han, I., Lee, K.C.: Ordinal Pairwise Partitioning (OPP) Approach to Neural Networks Training in Bond Rating. Intelligent Systems in Accounting, Finance and Management 6 (1997) 23-40
  9. Mukherjee, S., Osuna, E., Girosi, F.: Nonlinear prediction of chaotic time series using support vector machines. Proc. of the IEEE Workshop on Neural Networks for Signal Processing (1997) 511-520
  10. Platt, J.C., Cristianini, N., Shawe-Taylor, J.: Large Margin DAG's for multiclass classification. In Solla, S.A., Leen, T.K., Muller, K.-R.: Advances in Neural Information Processing Systems 12. MIT Press. Cambridge, MA (2000) 547-553
  11. Shin, K.S., Han, I.: A Case-based Approach using Inductive Indexing for Corporate Bond Rating. Decision Support Systems 32 (2001) 41-52 https://doi.org/10.1016/S0167-9236(01)00099-9
  12. Statnikov, A., Aliferis, C.F., Tsamardinos, I., Hardin, D., Levy, S.: A Comprehensive Evaluation of Multicategory Classification Methods for Microarray Gene Expression Cancer Diagnosis. Bioinformatics 21(5) (2005) 631-543 https://doi.org/10.1093/bioinformatics/bti033
  13. Tay, F.E.H, Cao, L.J.: Application of Support Vector Machines in Financial Time Series Forecasting. Omega 29 (2001) 309-317 https://doi.org/10.1016/S0305-0483(01)00026-3
  14. Vapnik, V.: The Nature of Statistical Learning Theory. Springer- Verlag. New York (1995)
  15. Vapnik, V.N.: Statistical Learning Theory. Wiley. New York (1998)
  16. Weston, J., Watkins, C: Support Vector Machines for Multiclass Pattem Recognition. Proc. of the Seventh European Symposium on Artificial Neural Networks (1999) 219-224
  17. Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann Publishers. San Francisco, CA (2000)