C-Support Vector Classification: Selection of kernel and parameters in medical diagnosis

This paper investigates the impact of kernel function and parameters of C-Support Vector Classification (C-SVC) to solve biomedical problems in a variety of clinical domains. Experimental results demonstrate the effectiveness of optimizing parameters for C-SVC with different basic kernel. Without optimizing parameters results for classification accuracy with data sets in medical domains shows the best performance of linear kernel. After optimization of parameters, results of classification accuracy are more consistent for all kernel functions, and we no longer have the dominance of certain kernel functions, or larger variance in the results. The biggest benefits of optimization had those kernel functions, which have a smaller accuracy of classification. Results show that time taken to build model are very high with C-SVC and polynomial kernel, compare with others kernels.

[1]  W I Schöllhorn,et al.  Applications of artificial neural nets in clinical biomechanics. , 2004, Clinical biomechanics.

[2]  Terrence J. Sejnowski,et al.  Comparison of machine learning and traditional classifiers in glaucoma diagnosis , 2002, IEEE Transactions on Biomedical Engineering.

[3]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[4]  Joarder Kamruzzaman,et al.  Overview of artificial neural networks and their applications in healthcare , 2006 .

[5]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[6]  Hsuan-Tien Lin.  A Study on Sigmoid Kernels for SVM and the Training of non-PSD Kernels by SMO-type Methods , 2005 .

[7]  Hussein A. Abbass,et al.  An evolutionary artificial neural networks approach for breast cancer diagnosis , 2002, Artif. Intell. Medicine.

[8]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[9]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[10]  C.W. Anderson,et al.  Comparison of linear, nonlinear, and feature selection methods for EEG signal classification , 2003, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[11]  Arthur Petrosian,et al.  Power frequency and wavelet characteristics in differentiating between normal and Alzheimer EEG , 2002, Proceedings of the Second Joint 24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society] [Engineering in Medicine and Biology.

[12]  Khosrow Behbehani,et al.  Neural Networks in Processing and Analysis of Biomedical Signals , 2012 .

[13]  Bernhard E. Boser,et al.  A training algorithm for optimal margin classifiers , 1992, COLT '92.

[14]  M. Aizerman,et al.  Theoretical Foundations of the Potential Function Method in Pattern Recognition Learning , 1964 .

[15]  Chih-Jen Lin,et al.  Asymptotic Behaviors of Support Vector Machines with Gaussian Kernel , 2003, Neural Computation.