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All Journal Bulletin of Electrical Engineering and Informatics
Benjamin Asubam Weyori
University of Energy and Natural Resources
Electrical & Electronics Engineering

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An empirical assessment of different kernel functions on the performance of support vector machines Isaac Kofi Nti; Owusu Nyarko-Boateng; Felix Adebayo Adekoya; Benjamin Asubam Weyori
Bulletin of Electrical Engineering and Informatics Vol 10, No 6: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i6.3046

Abstract

Artificial intelligence (AI) and machine learning (ML) have influenced every part of our day-to-day activities in this era of technological advancement, making a living more comfortable on the earth. Among the several AI and ML algorithms, the support vector machine (SVM) has become one of the most generally used algorithms for data mining, prediction and other (AI and ML) activities in several domains. The SVM’s performance is significantly centred on the kernel function (KF); nonetheless, there is no universal accepted ground for selecting an optimal KF for a specific domain. In this paper, we investigate empirically different KFs on the SVM performance in various fields. We illustrated the performance of the SVM based on different KF through extensive experimental results. Our empirical results show that no single KF is always suitable for achieving high accuracy and generalisation in all domains. However, the gaussian radial basis function (RBF) kernel is often the default choice. Also, if the KF parameters of the RBF and exponential RBF are optimised, they outperform the linear and sigmoid KF based SVM method in terms of accuracy. Besides, the linear KF is more suitable for the linearly separable dataset.
Co-Authors Felix Adebayo Adekoya Isaac Kofi Nti Owusu Nyarko-Boateng