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

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

Published in International Journal of Wireless Communications and Mobile Computing (Volume 7, Issue 1)
DOI 10.11648/j.wcmc.20190701.11
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), 2019. Published by Science Publishing Group

Keywords

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

References
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Cite This Article
  • 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

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    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

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    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

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  • @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}
    }
    

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  • 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  - 

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Author Information
  • Department of Electrical and Electronics Engineering, University of Agriculture, Makurdi, Nigeria

  • Department of Electrical and Electronics Engineering, University of Agriculture, Makurdi, Nigeria

  • Department of Electrical and Electronics Engineering, University of Agriculture, Makurdi, Nigeria

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