Abstract
In this paper, a non-balanced binary tree is proposed for extending support vector machines (SVM) to multi-class problems. The non-balanced binary tree is constructed based on the prior distribution of samples, which can make the more separable classes separated at the upper node of the binary tree. For an k class problem, this method only needs k-1 SVM classifiers in the training phase, while it has less than k binary test when making a decision. Further, this method can avoid the unclassifiable regions that exist in the conventional SVMs. The experimental result indicates that maintaining comparable accuracy, this method is faster than other methods in classification.
This work was supported by United Project of Yang Zi delta integration (2005E60007).
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References
Vapnik, V.N.: Statistical Learning Theory. John Wiley & Sons, Chichester (1998)
Platt, J.C., Cristianini, N., Shawe-Taylor, J.: Large Margin DAGs for Multiclass Classification. Advances in Neural Information Processing Systems 12, 547–553 (2000)
Friedman, J.: Another approach to polychotomous classification. Stanford University, Department of Statistics (1996)
Hsu, C.W., Lin, C.J.: A comparison of methods for multiclass support vector machines. IEEE Trans. Neural Networks 13, 415–425 (2002)
Shi, Z.H., Wang, X.D., Zhao, S.M., Yang, J.X.: An Improved Algorithm for SVM Decision Tree. Journal of Air Force Engineering University 7, 32–35 (2006)
Platt, J.: Sequential minimal optimization: A fast algorithm for training support vector machines. Technical Report 98-14, Microsoft Research, Redmond, Washington (April 1998)
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Xia, S., Li, J., Xia, L., Ju, C. (2007). Tree-Structured Support Vector Machines for Multi-class Classification . In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4493. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72395-0_50
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DOI: https://doi.org/10.1007/978-3-540-72395-0_50
Publisher Name: Springer, Berlin, Heidelberg
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