Comparative Analysis and Investigations of Various SVM Kernels Using Cellular Network KPI Data
International Journal of Wireless Communications and Mobile Computing
Volume 7, Issue 1, June 2019, Pages: 1-12
Received: Jan. 18, 2019;
Accepted: Mar. 12, 2019;
Published: Mar. 29, 2019
Views 571 Downloads 66
Omolaye Omohimire Philip, Department of Electrical and Electronics Engineering, University of Agriculture, Makurdi, Nigeria
Mom Joseph Michael, Department of Electrical and Electronics Engineering, University of Agriculture, Makurdi, Nigeria
Igwue Agwu Gabriel, Department of Electrical and Electronics Engineering, University of Agriculture, Makurdi, Nigeria
Classification plays a major role in every field in of human endeavors which Support Vector Machine (SVM) happened to be one of the popular algorithms for classification and prediction. However, the performance of SVM is greatly affected by the choice of a kernel function among other factors. In this research work, SVM is employed and evaluated with six different kernels by varying their parameters especially the training ratio to investigate their performance. The training ratio was varied in the proportion of 60-20-20, 40-30-30 and 20-40-40 to obtain higher classification accuracy. Based on the performance result, GRBK and ERBK kernels are capable of classifying datasets at hand accurately with the best specificity and sensitivity values. From the study, the SVM model with GRBF and ERBF kernels are the best suited for call algorithm data at hand in terms of best specificity and sensitivity values, followed by the RBF kernel. Also, the research further indicates that MLP, polynomial and linear kernels have worse performance. Therefore, despite SVMs being limited to making binary classifications, their superior properties of scalability and generalization capability give them an advantage in other domains.
Omolaye Omohimire Philip,
Mom Joseph Michael,
Igwue Agwu Gabriel,
Comparative Analysis and Investigations of Various SVM Kernels Using Cellular Network KPI Data, International Journal of Wireless Communications and Mobile Computing.
Vol. 7, No. 1,
2019, pp. 1-12.
Babu, R, Gowrishankar, H. S and Satyanarayana, P, “An Analytical framework for Call Admission Control in Heterogeneous Wireless Networks”, IJCSNS International Journal of 162 Computer Science and Network Security, VOL.9 No.10, October 2009, pp.162-166.
Bordes, A., Bottou, L., and Gallinari, P. Sgd-qn: Careful quasi-newton stochastic gradient descent. JMLR, 2009.
Deshmukh, S. B and Deshmukh, V, V (2013), Call Admission Control in Cellular Network, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, Vol. 2, Issue 4, April 2013, ISSN (Print): 2320 – 3765, ISSN (Online): 2278 – 8875.
Haykin, S. (2009). Neural networks and learning machines. 3rd ed. New Jersey: Pearson Education Inc.
Hoang N. D, Tien Bui D, Liao KW. 2016. Groutability estimation of grouting processes with cement grouts using differential flower pollination optimized support vector machine. Appl Soft Comput. 45:173–186.
Kaitao L, Natalie T, and Aidan, O. (2018). Artificial Intelligence and Machine Learning in Bioinformatics. Encyclopedia of Bioinformatics and Computational Biology doi:10.1016/B978-0-12-809633-8.20325-7.
Kotsiantis, S, Kanellopoulos, D and P. Pintelas, Data Preprocessing for Supervised Leaning. International Journal of Computer Science, Vol. 1, Issue No. 2, 2006; p. 111-117.
Kumar, S., Kumar, K and Kumar, P. (2014), A Comparative Study of Call Admission Control in Mobile Multimedia Networks using Soft Computing, International Journal of Computer Applications (0975 – 8887), Volume 107 – No. 16, December 2014.
Lin, S. W; Lee, Z. J; Chen, S. C. and Tseng, T. Y. “Parameter determination of support vector machine and feature selection using simulated annealing approach,” Applied Soft Computing Journal, vol. 8, no. 4, pp. 1505–1512, 2008.
Muthu R. K, Shuvo Banerjee, Chinmay Chakraborty, Chandan Chakraborty, Ajoy K. Ray, “Statistical analysis of mammographic features and its classification using support vector machine”, Expert Systems with Applications 37, page: 470-478, 2010.
Omolaye P. O., (2014): Management Information Systems Demystified for Managers and Professionals; Selfers Publishers Ltd., Makurdi, Nigeria. ISBN: 978-978-52663-75.
Omolaye, P. O., Mom, J. M. and Igwue, G. A (2017). A Holistic Review of Soft Computing Techniques. Applied and Computational Mathematics; 6(2): 93-110. http://www.sciencepublishinggroup.com/j/acm. doi: 10.11648/j.acm.20170602.15. ISSN: 2328-5605 (Print); ISSN: 2328-5613 (Online).
Qiujun H, Jingli M and Yong L. (2012). An Improved Grid Search Algorithm of SVR Parameters Optimization. Inst. of Elect and Elect Engineering (IEEE). 978-1-4673-2101-3/12.
Shanthini, D., Shanthi, M. and Bhuvaneswari, M. C. (2015). A Comparative Study of SVM Kernel Functions Based on Polynomial Coefficients and V-Transform Coefficients. International Journal of Engineering and Computer Science ISSN:2319-7242 Volume 6 Issue 3 March 2017, Page No. 20765-20769 Index Copernicus value (2015): 58.10 DOI:10.18535/ijecs/v6i3.65.
Singh R. K. and A. Ashtana (2012), “Architecture of wireless network,” International Journal of Soft Computing and Engineering, ISSN: 2231-2307, Volume-2, Issue-1.
Wu, C. F., Lee, L. T., Chang, H. Y. and Tao, D. F (2011), “A novel call admission control policy using mobility prediction and throttle mechanism for supporting QoS in wireless cellular networks,” Journal of Control Science and Engineering, Volume 2011, Article ID 190643, Hindawi Publishing Corporation, doi:10.1155/2011/190643.
Xin W, Arunita J and Ataul Bari (2011a), Optimal Channel Allocation with Dynamic Power Control in Cellular Networks, International Journal of Computer Networks & Communications (IJCNC) Vol.3, No.2, March 2011 DOI: 10.5121/ijcnc.2011.3206 83.
Yukai Y, Hongmei Cui, Yang Liu, Longjie Li, Long Zhang, and Xiaoyun Chen (2015). PMSVM: An Optimized Support Vector Machine Classification Algorithm Based on PCA and Multilevel Grid Search Methods. Hindawi Publishing Corporation, Mathematical Problems in Engineering, Volume 2015, Article ID 320186.
Zhi-ming W, Xian-sheng T, Zhe-ming Y, and Chao-hua W, “Parameters Optimization of SVM Based on Self-calling SVR,” Journal of System Simulation, 2010. 2, vol. 22 No. 2.
Chang Y, Hsieh C, Chang K. W, Ringgaard M and Lin C (2010). Training and testing low-degree polynomial data mappings via linear SVM. Journal of Machine Learning Research. 11:1471-1490.
Yoav G and Elhadad M. (2008). SplitSVM: fast, space-efficient, non-Heuristic, polynomial kernel computation for NLP applications. Proc. ACL-08:HTL.