Comparative Analysis and Investigations of Various SVM Kernels Using Cellular Network KPI Data

Omolaye Omohimire Philip; Mom Joseph Michael; Igwue Agwu Gabriel

doi:doi:10.11648/j.wcmc.20190701.11

| Peer-Reviewed |

Comparative Analysis and Investigations of Various SVM Kernels Using Cellular Network KPI Data

Omolaye Omohimire Philip, Mom Joseph Michael, Igwue Agwu Gabriel

Received: 18 January 2019 Accepted: 12 March 2019 Published: 29 March 2019

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

Abstract

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.

DOI	10.11648/j.wcmc.20190701.11
Published in	International Journal of Wireless Communications and Mobile Computing (Volume 7, Issue 1, June 2019)
Page(s)	1-12
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Support Vector Machine, Call Admission Control, Quality of Service, Radio Resource Management, Computational Intelligence and Artificial Intelligence

References

[1]	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.
[2]	Bordes, A., Bottou, L., and Gallinari, P. Sgd-qn: Careful quasi-newton stochastic gradient descent. JMLR, 2009.
[3]	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.
[4]	Haykin, S. (2009). Neural networks and learning machines. 3rd ed. New Jersey: Pearson Education Inc.
[5]	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.
[6]	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.
[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.
[8]	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.
[9]	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.
[10]	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.
[11]	Omolaye P. O., (2014): Management Information Systems Demystified for Managers and Professionals; Selfers Publishers Ltd., Makurdi, Nigeria. ISBN: 978-978-52663-75.
[12]	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).
[13]	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.
[14]	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.
[15]	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.
[16]	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.
[17]	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.
[18]	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.
[19]	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.
[20]	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.
[21]	Yoav G and Elhadad M. (2008). SplitSVM: fast, space-efficient, non-Heuristic, polynomial kernel computation for NLP applications. Proc. ACL-08:HTL.

Cite This Article

Plain Text BibTeX RIS

APA Style

Omolaye Omohimire Philip, Mom Joseph Michael, Igwue Agwu Gabriel. (2019). Comparative Analysis and Investigations of Various SVM Kernels Using Cellular Network KPI Data. International Journal of Wireless Communications and Mobile Computing, 7(1), 1-12. https://doi.org/10.11648/j.wcmc.20190701.11

Copy | Download

ACS Style

Omolaye Omohimire Philip; Mom Joseph Michael; Igwue Agwu Gabriel. Comparative Analysis and Investigations of Various SVM Kernels Using Cellular Network KPI Data. Int. J. Wirel. Commun. Mobile Comput. 2019, 7(1), 1-12. doi: 10.11648/j.wcmc.20190701.11

Copy | Download

AMA Style

Omolaye Omohimire Philip, Mom Joseph Michael, Igwue Agwu Gabriel. Comparative Analysis and Investigations of Various SVM Kernels Using Cellular Network KPI Data. Int J Wirel Commun Mobile Comput. 2019;7(1):1-12. doi: 10.11648/j.wcmc.20190701.11

Copy | Download

@article{10.11648/j.wcmc.20190701.11,
  author = {Omolaye Omohimire Philip and Mom Joseph Michael and Igwue Agwu Gabriel},
  title = {Comparative Analysis and Investigations of Various SVM Kernels Using Cellular Network KPI Data},
  journal = {International Journal of Wireless Communications and Mobile Computing},
  volume = {7},
  number = {1},
  pages = {1-12},
  doi = {10.11648/j.wcmc.20190701.11},
  url = {https://doi.org/10.11648/j.wcmc.20190701.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.wcmc.20190701.11},
  abstract = {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.},
 year = {2019}
}

Copy | Download

TY  - JOUR
T1  - Comparative Analysis and Investigations of Various SVM Kernels Using Cellular Network KPI Data
AU  - Omolaye Omohimire Philip
AU  - Mom Joseph Michael
AU  - Igwue Agwu Gabriel
Y1  - 2019/03/29
PY  - 2019
N1  - https://doi.org/10.11648/j.wcmc.20190701.11
DO  - 10.11648/j.wcmc.20190701.11
T2  - International Journal of Wireless Communications and Mobile Computing
JF  - International Journal of Wireless Communications and Mobile Computing
JO  - International Journal of Wireless Communications and Mobile Computing
SP  - 1
EP  - 12
PB  - Science Publishing Group
SN  - 2330-1015
UR  - https://doi.org/10.11648/j.wcmc.20190701.11
AB  - 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.
VL  - 7
IS  - 1
ER  -

Copy | Download

Author Information

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

Download PDF

Sections

Plain Text BibTeX RIS

APA Style

Omolaye Omohimire Philip, Mom Joseph Michael, Igwue Agwu Gabriel. (2019). Comparative Analysis and Investigations of Various SVM Kernels Using Cellular Network KPI Data. International Journal of Wireless Communications and Mobile Computing, 7(1), 1-12. https://doi.org/10.11648/j.wcmc.20190701.11

Copy | Download

ACS Style

Omolaye Omohimire Philip; Mom Joseph Michael; Igwue Agwu Gabriel. Comparative Analysis and Investigations of Various SVM Kernels Using Cellular Network KPI Data. Int. J. Wirel. Commun. Mobile Comput. 2019, 7(1), 1-12. doi: 10.11648/j.wcmc.20190701.11

Copy | Download

AMA Style

Omolaye Omohimire Philip, Mom Joseph Michael, Igwue Agwu Gabriel. Comparative Analysis and Investigations of Various SVM Kernels Using Cellular Network KPI Data. Int J Wirel Commun Mobile Comput. 2019;7(1):1-12. doi: 10.11648/j.wcmc.20190701.11

Copy | Download

@article{10.11648/j.wcmc.20190701.11,
  author = {Omolaye Omohimire Philip and Mom Joseph Michael and Igwue Agwu Gabriel},
  title = {Comparative Analysis and Investigations of Various SVM Kernels Using Cellular Network KPI Data},
  journal = {International Journal of Wireless Communications and Mobile Computing},
  volume = {7},
  number = {1},
  pages = {1-12},
  doi = {10.11648/j.wcmc.20190701.11},
  url = {https://doi.org/10.11648/j.wcmc.20190701.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.wcmc.20190701.11},
  abstract = {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.},
 year = {2019}
}

Copy | Download

TY  - JOUR
T1  - Comparative Analysis and Investigations of Various SVM Kernels Using Cellular Network KPI Data
AU  - Omolaye Omohimire Philip
AU  - Mom Joseph Michael
AU  - Igwue Agwu Gabriel
Y1  - 2019/03/29
PY  - 2019
N1  - https://doi.org/10.11648/j.wcmc.20190701.11
DO  - 10.11648/j.wcmc.20190701.11
T2  - International Journal of Wireless Communications and Mobile Computing
JF  - International Journal of Wireless Communications and Mobile Computing
JO  - International Journal of Wireless Communications and Mobile Computing
SP  - 1
EP  - 12
PB  - Science Publishing Group
SN  - 2330-1015
UR  - https://doi.org/10.11648/j.wcmc.20190701.11
AB  - 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.
VL  - 7
IS  - 1
ER  -

Copy | Download