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
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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.
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