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http://dx.doi.org/10.5391/IJFIS.2006.6.4.331

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)
Publication Information
International Journal of Fuzzy Logic and Intelligent Systems / v.6, no.4, 2006 , pp. 331-334 More about this Journal
Abstract
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.
Keywords
Multiclass SVM; Order information; Bond rating;
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1 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
2 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
3 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
4 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
5 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   DOI   ScienceOn
6 Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann Publishers. San Francisco, CA (2000)
7 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   DOI   ScienceOn
8 Vapnik, V.: The Nature of Statistical Learning Theory. Springer- Verlag. New York (1995)
9 Vapnik, V.N.: Statistical Learning Theory. Wiley. New York (1998)
10 Drucker, H., Wu, D., Vapnik, V.N.: Support vector machines for spam categorization. IEEE Transactions on Neural Networks, 10(5) (1999) 1048-1054   DOI   ScienceOn
11 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
12 Tay, F.E.H, Cao, L.J.: Application of Support Vector Machines in Financial Time Series Forecasting. Omega 29 (2001) 309-317   DOI   ScienceOn
13 Weston, J., Watkins, C: Support Vector Machines for Multiclass Pattem Recognition. Proc. of the Seventh European Symposium on Artificial Neural Networks (1999) 219-224
14 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
15 Shin, K.S., Han, I.: A Case-based Approach using Inductive Indexing for Corporate Bond Rating. Decision Support Systems 32 (2001) 41-52   DOI   ScienceOn
16 Hsu, C-W., Lin, C.-J.: A Simple Decomposition Method for Support Vector Machines. Machine Learning 46 (2002) 291-314   DOI
17 Friedman, J.: Another Approach to Polychtomous Classfication. Technical Report, Stanford University (1996)