Multi-class Classifier based on Support Vector Machine with Application to Ordinal Data
Support vector machine initially developed to perform binary classification. This paper presents a multi-class support vector machine classifier and ordinal regression to classify the type of bone mineral density. This paper compares the performance of four multi-class approaches, one-against-all, one-against-one, Weston and Watkins, and Crammer and Singer. Results from our real life data conclude that Crammer and Singer may be better approach depending on training error and the percentage of correctly classified test data. Also, we fined that the training error become more less when the regulization parameter and kernel parameter become large.
Al-Jumaily, H., H., 2010, “Assessment for Osteoporotic Women in Mosul City”, MSc thesis, College of Nursing, Mosul University.
Crammer, K. and Singer, Y., 2000, “On the Learnability and Design of Output Codes for Multi Class Problems”, Computational Learning Theory, pp.35-46.
Hsu, C., W. and Lin, C., J., 2002, “A comparison of Methods for Multi-class Support Vector Machines”, IEEE Transactions on Neural Networks, Vol. 13, pp.415-425.
Hu, X. and Pan, Y., 2007, “Knowledge Discovery in Bioinformatics, Techniques, Methods, and Applications”, John Wiley & Sons, INC., NJ.
Ivanciuc, O., 2007, “Application of Support Vector Machines in Chemistry”, Reviews Computational Chemistry, Vol.23, pp.291-400.
Karatzoglou, A. and Meyer, D., 2006, “Support Vector Machines in R”, Journal of Statistical Software, Vol.15, Iss. 9, pp.1-28.
Kleinbaum, D.,G. and Klein, M., 2010, “Logistic Regression A self Learning Text”, 3rd ed., Springer Science + Business Media LLC, NY.
Liang, Y., Xu, Q., Li, H., and Cao, D., 2011, “Support Vector Machines and Their Application in Chemistry and Biotechnology”, Taylor and Francis Group, LLC., NY.
Monfrini, E. and Guermeur, Y., 2011, “A Quadratic Loss Multi-Class Support Vector Machine for Which a Rdius-Margin Bound Applies”, Informatica, Vol.22, No.1, pp.73-96.
Sangeetha, R. and Kalpana, B, 2011, “Performance Evaluation of Kernels in Multiclass Support Vector Machines”, International Journal of Soft Computing and Engineering, Vol.1,Iss.5, pp.138-145.
Seeja, K.,R. and Shweta, L., 2011, “Microarray Data Classification Using Support Vector Machines”, International Journal of Biometrics and Bioinformatics, Vol.5, Iss.1, pp.10-15.
Statnikov, A., Aliferis, C., F., Hardin, D., P., and Guyon, I., 2011, “A Gentle Introduction to Support Vector Machines in Biomedicine”, World Scientific Publishing Co, Pte., Ltd., Singapore.
Vapnik, V., 2010, “The Nature of Statistical Learning Theory”, 2nd ed., Springer-Verlage New York, Inc., NY.
Westone, J. and Watkins, C., 1999, “Support Vector Machine for Multi-Class Pattern Recognition”, Proceeding of the 7th European Symposium on Artificial Neural Networks.
Xia, F., Zhou, L., Yang, Y., and Zhang, W., 2007, “Ordinal Regression as Multiclass Classification”, International Journal of Intelligent Control and Systems, Vol.12, No.3, pp.230-236.
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