Open Access Open Access  Restricted Access Subscription or Fee Access

Improved Classification of Liver Disorder and Blood Transfusion Donor Data Using Mixed Kernel SVM

Pulak Sahoo

Abstract


Accurate detection and classification of various ailments has been a critical issue in the field of medical science and research since a long time. There are many mathematical, statistical and machine learning approaches exist for this purpose which results in limited success due to various reasons. This paper proposes an approach consisting of mixed kernel based support vector machine along with tuning of appropriate kernel parameters to improve classification performance. The mixed kernel based approach uses an iteratively determined optimal combination of different standard kernels, to produce better classification accuracy. Tuning of kernel parameters like polynomial degree in case of polynomial kernel and kernel width in case of Radial basis function kernel, is done iteratively to get best possible results. Experiments with two benchmark datasets, 1. BUPA liver disorders dataset and 2. Blood transfusion service center dataset, demonstrate the improvement in classification performance.

Keywords


Kernel Function, Mixed Kernel, Polynomial Function Radial Basis Function, Support Vector Machine.

Full Text:

PDF

References


. Theodoridis, S., Koutroumbas, K., ―Pattern Recognition‖, Academic Press, San Diego, CA 92101--4495, USA, 2008.

. Subashini, T.S., Ramalingam, V., Palanivel, S., ―Breast Mass Classification Based on Cytological Patterns Using RBFNN and SVM,‖ Expert Systems with Applications, 2008.

. Dhanalakshmi, P., Palanivel, S., Ramalingam, V., ―Classification of Audio Signals Using SVM and RBFNN,‖ In: Expert Systems with Applications, 2008.

. Kim, K., ―Financial time series forecasting using support vector machines,‖ Neurocomputing vol.55, no.1, 2003, pp.221--228

. Hammer, B. and Gersmann, K., ―A Note on the Universal Approximation Capability of Support Vector Machines,‖ Neural Processing Letters, vol.17,no.1, 1993, pp.43-53.

. Braga, P.L. and Oliveira, A.L.I. and Meira, S.R.L., ―A GA-based Feature Selection and Parameters Optimization for Support Vector Regression Applied to Software Effort Estimation,‖ Proceedings of the 2008 ACM symposium on Applied Computing, 2008, pp. 1788—1792.

. Belousov, AI and Verzakov, SA and Von Frese, J., ―A Flexible Classification Approach with Optimal Generalization performance: support vector machines,‖ Chemometrics and Intelligent Laboratory Systems, vol. 64, vol.1, 2002, pp.15-25.

. Cherkassky, V. and Ma, Y., ―Practical Selection of SVM Parameters and Noise Estimation for SVM Regression,‖ Neural Networks, vol.17,no.1, 2004, pp.113-126.

. Bishop, C.M., ―Neural Networks for Pattern Recognition,‖ Oxford University Press, New York, 1995.

. Chen, Hu., Xuli Zong., Lee, Chung-wei, Jyh-haw Yeh, ―World Wide Web Usage Mining Systems and Technologies,‖ Journal of Systemics, Cybernetics and Informatics, vol.1, no. 4, 2003, pp.53—59.

. Mo, Y. and Xu, S., ―Application of SVM Based on Hybrid Kernel Function in Heart Disease Diagnoses,‖ Intelligent Computing and Cognitive Informatics (ICICCI), 2010 International Conference, 2010, pp.462-465.

. Tan, X. and Bi, W. and Hou, X. and Wang, W., ―Reliability Analysis using Radial basis Function Networks and Support Vector Machines,‖ Computers and Geotechnics,vol.38,no.2, 2011, pp.178-186.

. http://en.wikipedia.org/wiki/Support_vector_machine

. Li, Z., He, P., Lei, M., ―Applying RBF Network to Web Classification Mining,‖ Journal of Communication and Computer, no.9, 2005, pp.22-24.

. Junjie, C. and Rongbing, H., ―Research of Web classification mining based on RBF neural network,‖ Control, Automation, Robotics and Vision Conference, 2004. ICARCV 2004 8th, vol.2, 2004, pp. 1365 – 1367.

. www.ip2location.com

. Xu, Q. and Geng, S., ―A Fast SVM Classification Learning Algorithm Used to Large Training Set,‖ Intelligent System Design and Engineering Application (ISDEA), 2012 Second International Conference, 2012, pp.15-19

. Hussain, M., Wajid, S.K, Elzaart, A. and Berbar, M., ―A Comparison of SVM Kernel Functions for Breast Cancer Detection,‖ Computer Graphics, Imaging and Visualization (CGIV), 2011 Eighth International Conference, 2011, pp.145-150.

. Lin, S.,Liu, Z., ―Parameter Selection in SVM with RBF Kernel function,‖ Journal-Zhejiang University of Technology,vol.35,no.2, 2012, pp.135.

. Prajapati, G.L. and Patle, A., ―On Performing Classification Using SVM with Radial Basis and Polynomial Kernel Functions.‖ Emerging Trends in Engineering and Technology (ICETET), 2010 3rd International Conference, 2010 , pp.512-515.

. Kun, Y. and Zhicheng, J., ―An Improved SVM-based Model with Peak Recognition for Electricity Demand Forecasting,‖ Control Conference (CCC), 2011, pp.3011-3014.

. Sahoo, P., Pradhan, D., ―Forecast of Diabetes and Diagnosis of Liver Disease Using Hybrid Kernel Based Support Vector Machine‖, International Journal of Trends in Computer Science ―(IJTICS), 2013

. Smith, J.W. and Everhart, JE and Dickson, WC and Knowler, WC and Johannes, RS, ―Using the ADAP learning algorithm to forecast the onset of diabetes mellitus Proceedings of the Annual Symposium on Computer Application in Medical Care,‖ American Medical Informatics Association, 1988

. http://archive.ics.uci.edu/ml/datasets/Liver+Disorders http://archive.ics.uci.edu/ml/datasets/Blood+Transfusion+Service+Center


Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.