Browse > Article
http://dx.doi.org/10.9717/kmms.2013.16.7.795

A Novel Feature Selection Method for Output Coding based Multiclass SVM  

Lee, Youngjoo (삼성전자 생산기술연구소)
Lee, Jeongjin (숭실대학교 컴퓨터학부)
Publication Information
Journal of Korea Multimedia Society / v.16, no.7, 2013 , pp. 795-801 More about this Journal
Abstract
Recently, support vector machine has been widely used in various application fields due to its superiority of classification performance comparing with decision tree and neural network. Since support vector machine is basically designed for the binary classification problem, output coding method to analyze the classification result of multiclass binary classifier is used for the application of support vector machine into the multiclass problem. However, previous feature selection method for output coding based support vector machine found the features to improve the overall classification accuracy instead of improving each classification accuracy of each classifier. In this paper, we propose the novel feature selection method to find the features for maximizing the classification accuracy of each binary classifier in output coding based support vector machine. Experimental result showed that proposed method significantly improved the classification accuracy comparing with previous feature selection method.
Keywords
Feautre Selection; Multiclass; Output Coding; Support Vector Machine;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 C. Huang, L.S. Davis, and J.R.G. Townshend, "An Assessment of Support Vector Machines for land Cover Classification," International Journal of Remote Sensing, Vol. 23, No. 4, pp. 725-749, 2002.   DOI   ScienceOn
2 Y. Bazi and F. Melgani, "Toward an Optimal SVM Classification System for Hyperspectral Remote Sensing Images," IEEE Transactions on Geoscience and Remote Sensing, Vol. 44, No. 11, pp. 3374-3385, 2006   DOI   ScienceOn
3 K. Crammer and Y. Singer, "On the Learnability and Design of Output Codes for Multiclass Problems," Computational Learning Theory, Vol. 47, No. 2, pp. 35-46, 2000.
4 J. Weston and C. Watkins, "Multi-Class Support Vector Machines," Proc. of European Symposium on Artificial Neural Networks, pp. 219-224, 1999.
5 M. Bartlett, J. Movellan, and T. Sejnowski, "Face Recognition by Independent Component Analysis," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 13, No. 6, pp. 1450-1464, 2002.
6 J.C. Platt, N. Cristianini, and J. Shawe-Taylor, "Large Margin DAG's for Multiclass Classification," Advances in Neural Information Processing Systems, Vol. 12, pp. 547-553, MIT Press, 2000.
7 B. Fei and J. Liu, "Binary tree of SVM: A New Fast Multiclass Training and Classification Algorithm," IEEE Transactions on Neural Networks, Vol. 17, No. 3, pp. 696-704, 2006.   DOI   ScienceOn
8 M. Kudo and J. Sklansky, "Comparison of Algorithms that Select Features for Pattern Classifiers," Pattern Recognition, Vol. 33, No. 1, pp. 25-41, 2000.   DOI   ScienceOn
9 UCI Machine Learning Repository, http://archive.ics.uci.edu/ml/, 2013.
10 I. Guyon and A. Elisseeff, "An Introduction to Variable and Feature Selection," Journal of Machine Learning Research, Vol. 3, No. 1, pp. 1157- 1182, 2003.(호 기입요함!)
11 J.H. Friedman, Another Approach to Polychotomous Classification, Technical Report, Stanford University, 1996.
12 A. Jain and D. Zongker, "Feature Selection: Evaluation, Application, and Small Sample Performance," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 19, No. 2, pp. 153-158, 1997.   DOI   ScienceOn
13 이정진, 김경원, 이호, "블록 기반 클러스터링과 히스토그램 카이 제곱 거리를 이용한 반도체 결함 원인 진단 기법," 멀티미디어학회논문지, 제5권, 제9호, pp. 1149-1155, 2012.   과학기술학회마을   DOI   ScienceOn
14 G.F. Hughes, "On the Mean Accuracy of Statistical Pattern Recognizers," IEEE Transactions on Information Theory, pp. 55-63, 1968.
15 Y. Saeys, I. Inza, and P. Larranaga, "A Review of Feature Selection Techniques in bioinformatics," Bioinformatics, Vol. 23, No. 19, pp. 2507-2517, 2007.   DOI   ScienceOn
16 A.L. Blum and R.L. Rivest, "Training a 3- Node Neural Network is NP-complete," Neural Networks, Vol. 5, No. 1, pp. 17-27, 1992.
17 M. Garey and D. Johnson, Computers and Intractability: A Guide to the Theory of NPcompleteness, Freeman, New York, 1979.
18 C. Hsu and C. Lin, "A Comparison of Methods for Multiclass Support Vector Machines," IEEE Transactions on Neural Networks, Vol. 13, No. 2, pp. 415-425, 2002.   DOI   ScienceOn
19 S. Cheong, S.H. Oh, and S. Lee, "Support Vector Machines with Binary Tree Architecture for Multi-Class Classification," Neural Information Processing: Letters and Reviews, Vol. 2, No. 3, pp. 47-51, 2004.
20 C. Young, C. Yen, Y. Pao, and M. Nagurka, "One-class-at-a-time Removal Sequence Planning Method for Multiclass Classification Problems," IEEE Transactions on Neural Networks, Vol. 17, No. 6, pp. 1544-1549, 2006.   DOI   ScienceOn
21 J. Ghosh, "Multiclassifier Systems: Back to the Future," Proc. of the 3rd Int'l Workshop on Multiple Classifier Systems, Lecture Note in Computer Science, Vol. 2364, pp. 1-15, 2002.
22 T. Hastie and R. Tibshirani, "Classification by Pairwise Coupling," The Annals of Statistics, Vol. 26, No. 2, pp. 451-471, 1998.   DOI
23 T. Dietterich and G. Bakiri, "Solving Multiclass Learning Problems Via Error-Correcting Output Codes," Journal of Artificial Intelligence Research, Vol. 2, pp. 263-286, 1995.