In this paper, a new version of support vector regression (SVR) is presented namely Fuzzy Cost SVR (FCSVR). Individual property of the FCSVR is operation over fuzzy data whereas fuzzy cost (fuzzy margin and fuzzy penalty) are maximized. This idea admits to have uncertainty in the penalty and margin terms jointly. Robustness against noise is shown in the experimental results as a property of the proposed method and superiority relative conventional SVR.
[1] C-f. Lin, S-d. Wang, "Fuzzy Support Vector Machine," IEEE Trans. on
Neural Networks, 2002, vol. 13, no. 2, pp. 464-471, Mar. 2002.
[2] C-f. Lin, S-d. Wang "Training Algorithms for Fuzzy Support Vector
Machines with Noisy Data," Pattern Recognition Letters, vol.25, pp.
1647-1656, 2004.
[3] D.H. Hong, C. Hwang, "Support vector fuzzy regression machines,"
Fuzzy Sets and Systems 138, pp.271-281, 2003.
[4] A-B. Ji, J-H. Pang, S-H. Li, J-P. Sun, "Support Vectro Machine for
Classification Based on Fuzzy Training Data," Proceedings of the Fifth
Int. Conf. on Machine Learning and Cybernetics, Dalian, pp.1609-1614,
13-16 August 2006.
[5] H.Sadoghi Yazdi, S. Effati, Z. Saberi, "The Probabilistic Constraints in
the Support Vector Machine," Applied Mathematics and Computation,
vol.194, no.2, p.467-479, Dec 2007 .
[6] Y-H. Liu, and Y-T. Chen, "Face Recognition Using Total Margin-Based
Adaptive Fuzzy Support Vector Machines," IEEE Trans. on Neural
Networks, vol. 18, no.1, pp.178-192, Jan. 2007.
[7] L. Chu, and C. Wu, "A Fuzzy Support Vector Machine Based on
Geometric Model," Proceedings of the fifth World Congress on
Intelligent Control and Automation, Hangzhou, P.R. China, pp.1843-
1846,June 15-19,2004.
[8] Y. Wang, S. Wang, and K. K. Lai, "A New Fuzzy Support Vector
Machine to Evaluate Credit Risk," IEEE Trans. on Fuzzy Systems,
vol.13 no.6, pp.820-831, Dec. 2005.
[9] Jayadeva, R. Khemchandani, S. andra, "Fuzzy Linear Proximal Support
Vector Machines for Multi-Category Data Classification,"
Neurocomputing, vol.67, pp.426-435, 2005.
[10] T-Y. Wang, H-M. Chiang, "Fuzzy Support Vector Machine for Multi-
Class Text Categorization," Information Processing and Management,
vol.43, pp.914-929, 2007.
[1] C-f. Lin, S-d. Wang, "Fuzzy Support Vector Machine," IEEE Trans. on
Neural Networks, 2002, vol. 13, no. 2, pp. 464-471, Mar. 2002.
[2] C-f. Lin, S-d. Wang "Training Algorithms for Fuzzy Support Vector
Machines with Noisy Data," Pattern Recognition Letters, vol.25, pp.
1647-1656, 2004.
[3] D.H. Hong, C. Hwang, "Support vector fuzzy regression machines,"
Fuzzy Sets and Systems 138, pp.271-281, 2003.
[4] A-B. Ji, J-H. Pang, S-H. Li, J-P. Sun, "Support Vectro Machine for
Classification Based on Fuzzy Training Data," Proceedings of the Fifth
Int. Conf. on Machine Learning and Cybernetics, Dalian, pp.1609-1614,
13-16 August 2006.
[5] H.Sadoghi Yazdi, S. Effati, Z. Saberi, "The Probabilistic Constraints in
the Support Vector Machine," Applied Mathematics and Computation,
vol.194, no.2, p.467-479, Dec 2007 .
[6] Y-H. Liu, and Y-T. Chen, "Face Recognition Using Total Margin-Based
Adaptive Fuzzy Support Vector Machines," IEEE Trans. on Neural
Networks, vol. 18, no.1, pp.178-192, Jan. 2007.
[7] L. Chu, and C. Wu, "A Fuzzy Support Vector Machine Based on
Geometric Model," Proceedings of the fifth World Congress on
Intelligent Control and Automation, Hangzhou, P.R. China, pp.1843-
1846,June 15-19,2004.
[8] Y. Wang, S. Wang, and K. K. Lai, "A New Fuzzy Support Vector
Machine to Evaluate Credit Risk," IEEE Trans. on Fuzzy Systems,
vol.13 no.6, pp.820-831, Dec. 2005.
[9] Jayadeva, R. Khemchandani, S. andra, "Fuzzy Linear Proximal Support
Vector Machines for Multi-Category Data Classification,"
Neurocomputing, vol.67, pp.426-435, 2005.
[10] T-Y. Wang, H-M. Chiang, "Fuzzy Support Vector Machine for Multi-
Class Text Categorization," Information Processing and Management,
vol.43, pp.914-929, 2007.
@article{"International Journal of Engineering, Mathematical and Physical Sciences:62821", author = "Hadi Sadoghi Yazdi and Tahereh Royani and Mehri Sadoghi Yazdi and Sohrab Effati", title = "Fuzzy Cost Support Vector Regression", abstract = "In this paper, a new version of support vector regression (SVR) is presented namely Fuzzy Cost SVR (FCSVR). Individual property of the FCSVR is operation over fuzzy data whereas fuzzy cost (fuzzy margin and fuzzy penalty) are maximized. This idea admits to have uncertainty in the penalty and margin terms jointly. Robustness against noise is shown in the experimental results as a property of the proposed method and superiority relative conventional SVR.
", keywords = "Support vector regression, Fuzzy input, Fuzzy cost.", volume = "2", number = "8", pages = "602-6", }
