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Case Study Application for C-Support Vector Classification: The Estimation ofMS Subgroup Classification with Selected Kernels and Parameters

Journal Name:

  • European Journal of Pure and Applied Mathematics

Publication Year:

  • 2016

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Abstract (2. Language): 
The study has classified the subgroups of Multiple Sclerosis (MS) using Support Vector Machines (SVM). C- Support Vector Classifier (C-SVC) algorithm, one of the SVM classifiers of multi class, has been utilized for the classification of MS subgroups. For this purpose, 120 MS patients (76 RRMS, 38 SPMS, 6 PPMS patients) have been included in the study. Through Magnetic Resonance Imaging (MRI), the number of lesion diameter and Expanded Disability Status Scale (EDSS) data are applied through C-SVC. Lesion data has been obtained from three separate regions of the brain which are brain stem, periventricular corpus callosum and upper cervical region. By applying the data onto Radial Basis Function kernel (RBF), Polynomial kernel, Sigmoid kernel and Linear kernel, four of the kernel types of C-SVC algorithm, the accuracy rates of MS subgroups classification and the computation time during the training procedure are computed and compared. By adding EDSS score into the dataset, the classification achievement rate has increased in all the kernel types based on the analyses conducted. Having applied C-SVC on MS subgroups, classification achievement of MS subgroups, namely that of RRMS, SPMS and PPMS has been measured.
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REFERENCES

References: 

[1] H. Alashwal, S. Deris, and R.M. Othman. A bayesian kernel for the prediction of proteinprotein
interactions. World Academy of Science, Engineering and Technology, 51:928–933,
2009.
[2] Alisneaky. Support vector machine — Wikipedia, the free encyclopedia, 2011. [Online;
accessed 17-April-2011].
[3] E. Alpaydın. Yapay Ö˘grenme. Bo˘gaziçi Üniversitesi Yayınevi, Istanbul, Turkey, 2013.
[4] J. Basak. A least square kernel machine with box constraints. In 19th International
Conference on Pattern Recognition, pages 1–4, Florida, USA, 2008. IEEE.
[5] K. Bendfeldt, S. Klöppel, T.E. Nichols, R. Smieskova, P. Kuster, S. Traud, N.M. Lenke,
Y. Naegelin, L. Kappos, E.W. Radue, and S.J. Borgwardt. Multivariate pattern classification
of gray matter pathology in multiple sclerosis. NeuroImage, 60(1):400–408, Mar
2012.
[6] R. Bergamaschi, S. Quaglini, M. Trojano, M.P. Amato, E. Tavazzi, D. Paolicelli, V. Zipoli,
A. Romani, A. Fuiani, E. Portaccio, C. Berzuini, C. Montomoli, S. Bastianello, and V. Cosi.
Early prediction of the long term evolution of multiple sclerosis: the bayesian risk estimate
for multiple sclerosis (BREMS) score. Journal of Neurology, Neurosurgery & Psychiatry,
78(7):757–759, Dec 2006.
[7] B.E. Boser, I.M. Guyon, and V.N. Vapnik. A training algorithm for optimal margin classifiers.
In Proceedings of the Fifth Annual Workshop on Computational Learning Theory,
COLT ’92, pages 144–152, New York, NY, USA, 1992. ACM.
REFERENCES 213
[8] S. Boughorbel, J. Tarel, and N. Boujema. Generalized histogram intersection kernel for
image recognition. In IEEE International Conference on Image Processing, volume 3, pages
III–161, Genoa, Italy, 2005. IEEE.
[9] C.C. Chang and C.J. Lin. LIBSVM: A library for support vector machines. ACM Transactions
on Intelligent Systems and Technology, 2:27:1–27:27, 2011.
[10] C. Cortes and V. Vapnik. Support-vector networks. Machine Learning, 20(3):273–29,
1995.
[11] S. Fomel. Inverse b-spline interpolation, 2000.
[12] M. Gaspari, G. Roveda, C. Scandellari, and S. Stecchi. An expert systemfor the evaluation
of EDSS in multiple sclerosis. Artificial Intelligence inMedicine, 25(2):187–210, Jun 2002.
[13] M.G. Genton. Classes of kernels for machine learning: a statistics perspective. The
Journal of Machine Learning Research, 2:299–312, 2002.
[14] H. Gutermana, Y. Nehmadi, A. Chistyakov, J.F. Soustiel, and M. Feinsod. A comparison
of neural network and bayes recognition approaches in the evaluation of the brainstem
trigeminal evoked potentials in multiple sclerosis. International Journal of Bio-Medical
Computing, 43(3):203–213, Dec 1996.
[15] B. Hamers. Kernel models for large scale applications. PhD thesis, Katholieke Universiteit
Leuven, 2004.
[16] C.H. Hawkes and G. Giovannoni. The McDonald criteria for multiple sclerosis: time for
clarification. Multiple Sclerosis Journal, 16(5):566–575, Mar 2010.
[17] C. Hirst, G. Ingram, R. Swingler, D.A.S. Compston, T. Pickersgill, and N.P. Robertson.
Change in disability in patients with multiple sclerosis: a 20-year prospective populationbased
analysis. Journal of Neurology, Neurosurgery & Psychiatry, 79(10):1137–1143, Oct
2008.
[18] T. Hofmann, B. Schölkopf, and A.J. Smola. Kernel methods in machine learning. The
annals of statistics, 36(3):1171–1220, 2008.
[19] T. Howley and M.G. Madden. The genetic kernel support vector machine: Description
and evaluation. Artificial Intelligence Review, 24(3-4):379–395, 2005.
[20] C-W Hsu and C-J Lin. A comparison of methods for multiclass support vector machines.
IEEE Transactions on Neural Networks, 13(2):415–425, Mar 2002.
[21] R. Karabudak. MS ile Ya¸samak. A¸sina Kitaplar, Turkey, 2006.
[22] R. Karabudak, S. Aksel, A. Altınba¸s, and D.Y. Ku¸sçu. Olgularla Multiple Skleroz. Bilimsel
Tıp Yayınevi, Ankara, Turkey, 2011.
REFERENCES 214
[23] Y. Karaca. Constituting an Optimum Mathematical Model for the Diagnosis of Multiple
Sclerosis. PhD thesis, Marmara University, 2012.
[24] Y. Karaca and ¸S. Hayta. The significance of artificial neural networks algorithms classification
in the multiple sclerosis and its subgroups. International Advanced Research
Journal in Science, Engineering and Technology (IARJSET), 2(12):1–7, Dec 2015.
[25] Y. Karaca, ¸S. Hayta, and R. Karabudak. The application of support vector machines for
the classification of multiple sclerosis subgroups. In The International Conference Mathematical
and Computational Modelling in Science and Technology, Abstract Book, page 95,
Izmir University, Izmir, 2015.
[26] Y. Karaca, O. Osman, and R. Karabudak. Linear modeling of multiple sclerosis and its
subgroubs. Journal of Clinical Research of Pediatric Endocrinology, 21(1):7–12, Mar 2015.
[27] Y. Karaca and G. Sayıcı. Bayesian networks for subgroups of multiple sclerosis. International
Journal of Electronics, Mechanical and Mechatronics Engineering (IJEMME),
3(1):455–462, 2013.
[28] Y. Karaca, G. Sayıcı, and R. Karabudak. Application of decision tree for classification
of multiple sclerosis diagnosis, expanded disability status scale and lesion numbers.
In 7th International Image Processing & Wavelet on real World applications conference
(IWW2013), pages 115–129, Valencia, 2013.
[29] C.A. Micchelli. Interpolation of scattered data: distance matrices and conditionally positive
definite functions. Constructive approximation, 2(1):11–22, 1986.
[30] J. Novakovic and A. Veljovic. C-support vector classification: Selection of kernel and
parameters in medical diagnosis. In IEEE 9th International Symposium on Intelligent
Systems and Informatics, pages 465–470, Subotica, Serbia, Sep 2011. IEEE.
[31] F. Parzlivand and A. Shahrezaee. Radial basis functions for the solution of an inverse
problem of mixed parabolic-hyperbolic type. European Journal of Pure and Applied Mathematics,
8(2):239–254, 2015.
[32] H. Sahbi and F. Fleuret. Kernel methods and scale invariance using the triangular kernel.
Technical Report RR-5143, French Institute for Research in Computer Science and
Automation, 2004.
[33] M. Shahlaei, A. Fassihi, and L. Saghaie. Application of PC-ANN and PC-LS-SVM in QSAR
of CCR1 antagonist compounds: A comparative study. European Journal of Medicinal
Chemistry, 45(4):1572–1582, Apr 2010.
[34] A. Vedaldi and Andrew A. Zisserman. Efficient additive kernels via explicit feature maps.
IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(3):480–492, 2012.
[35] L. Zhang, W. Zhou, and L. Jiao. Wavelet support vector machine. IEEE Transactions on
Systems, Man, and Cybernetics, Part B: Cybernetics, 34(1):34–39, 2004.
REFERENCES 215
[36] C.Y. Zhao, R.S. Zhang, H.X. Liu, C.X. Xue, S.G. Zhao, X.F. Zhou, M.C. Liu, and B.T. Fan. Diagnosing
anorexia based on partial least squares, back propagation neural network, and
support vector machines. Journal of Chemical Information and Modeling, 44(6):2040–
2046, Nov 2004.

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