The best separating decision tree twin support vector machine for multi-class classification

Procedia Computer Science

Volumn 17, Issue , 2013, Pages 1032-1038

  Shao, Yuan Hai ^a   Chen, Wei Jie ^a   Huang, Wen Biao ^a   Yang, Zhi Min ^a   Deng, Nai Yang ^b  

^a ZHEJIANG UNIVERSITY OF TECHNOLOGY   (China)

^b CHINA AGRICULTURAL UNIVERSITY   (China)

Author keywords

Decision tree; Multi class classification; The best separating principle; Twin support vector machine

Indexed keywords

EID: 84898741649     PISSN: 18770509     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1016/j.procs.2013.05.131     Document Type: Conference Paper

References (21)

1. V. N. Vapnik, The Nature of Statistical Learning Theory, Springer New York Inc., New York, NY, USA, 1995.
2. N. Y. Deng, Y. J. Tian, C. H. Zhang, Support Vector Machines: Optimization Based Theory, Algorithms, and Extensions, CRC Press, 2012.
3. Y. Tian, Y. Shi, X. Liu, Recent advances on support vector machines research, Technological and Economic Development of Economy 18 (1) (2012) 5-33.
4. Y. Li, Y. Shao, N. Deng, Improved prediction of palmitoylation sitesusing pwms and svm, Protein and Peptide Letters 18 (2) (2011) 186-193.
5. Y. C. Wang, Y. Wang, Z. X. Yang, N. Y. Deng, Support vector machine prediction of enzyme function with conjoint triad feature and hierarchical context, BMC systems biology 5 (Suppl 1) (2011) S6.
6. Y. Li, Y. Shao, L. Jing, N. Deng, An efficient support vector machine approach for identifying protein s-nitrosylation sites, Protein and Peptide Letters 18 (6) (2011) 573-587.
7. R. Jayadeva Khemchandani, S. Chandra, Twin support vector machines for pattern classification, IEEE Transactions on Pattern Analysis and Machine Intelligence 29 (2007) 905-910.
8. Y. H. Shao, C. H. Zhang, X. B. Wang, N. Y. Deng, Improvements on twin support vector machines., IEEE Transactions on Neural Networks 22 (6) (2011) 962-968.
9. Y. Shao, N. Deng, A novel margin-based twin support vector machine with unity norm hyperplanes, Neural Computing and Applications DOI 10.1007/s00521-012-0894-5 (2012) 1-9.
10. Z. Qi, Y. Tian, S. Yong, Robust twin support vector machine for pattern classification, Pattern Recognition 46(1) (2012) 305-316.
11. Y. H. Shao, N. Y. Deng, Z. M. Yang, Least squares recursive projection twin support vector machine for classification, Pattern Recognition 45 (6) (2012) 2299-2307.
12. Z. Qi, Y. Tian, S. Yong, Laplacian twin support vector machine for semi-supervised classification, Neural Networks 35 (2012) 46-53.
13. Y. Shao, N. Deng, Z. Yang, W. Chen, Z. Wang, Probabilistic outputs for twin support vector machines, Knowledge-Based Systems 33 (2012) 145-151.
14. Z. Qi, Y. Tian, S. Yong, Twin support vector machine with universum data, Neural Networks 36C (2012) 112-119. doi:doi: 10.1016/j.neunet.2012.09.004.
15. Y. Shao, N. Deng, W. Chen, Z. Wang, Improved generalized eigenvalue proximal support vector machine, IEEE Signal Processing Letters 20 (3) (2013) 213-216.
16. Z. Qi, Y. Tian, Y. Shi, Structural twin support vector machine for classification, Knowledge-Based Systemsdoi:DOI: 10.1016/j.knosys.2013.01.008.
17. Z.Wang, J. Chen, M. Qin, Non-parallel planes support vector machine for multi-class classification, in: Logistics Systems and Intelligent Management, 2010 International Conference on, Vol. 1, 2010, pp. 581-585.
18. H. H. Cong, C. F. Yang, X. R. Pu, Efficient speaker recognition based on multi-class twin support vector machines and gmms, 2008, pp. 348-352.
19. Z. X. Yang, Y. H. Shao, X. S. Zhang, Multiple birth support vector machine for multi-class classification, Neural Computing and Application DOI: 10.1007/s00521-012-1108-x (2012) 1-9.
20. O. L. Mangasarian, E. W. Wild, Multisurface proximal support vector machine classification via generalized eigenvalues., IEEE Transactions on Pattern Analysis and Machine Intelligence 28 (1) (2006) 69-74.
21. S. R. Safavian, D. Landgrebe, A survey of decision tree classifier methodology, IEEE Transactions on Systems, Man, andCybernetics 21 (3) (1991) 660-674.