Comparative Analysis of the Performance of Various Support Vector Machine kernels

Abstract

Support Vector Machine (SVM) in dealing with a classification problem, separates classes using decision boundaries with the primary objective of establishing a large margin between support vectors of the respective class groups; it utilizes kernels to achieve non-linear decision boundaries. This current work examines the performance of four SVM kernels (Sigmoid, Linear, Radial Basis Function (RBF) and Polynomial kernel functions) in addressing classification problems using two datasets from two domains. The two datasets are the Knowledge Discovery in Dataset (KDD) and a set of features extracted from voiced and unvoiced frames. The Polynomial kernel function had the best classification performance on the KDD dataset with accuracy and precision of 99.77% and 99.8% respectively but recorded the worst performance against the voice-feature dataset with an accuracy of 74.96%. Inductively, the polynomial kernel can be best suited for some classification datasets but can return the worst classification performance on another classification dataset. The RBF shows consistent high performance across the two data domains with accuracies of 96.04% and 99.77% and can be considered a general-purpose kernel guaranteed to yield satisfactory classification performance regardless of the dataset type or data domains. The performance of polynomial kernels over the two separate datasets supports the “No Free Launch Theorem”, which when applied to machine learning, means that if an algorithm performs well over a class of problem, it may have worse performance on other class of problem. This implies that there might not be one specific machine learning algorithm that gives the best possible performance for a set of problems, it is therefore important for researchers to try out various algorithms before concluding on the best possible result on any dataset.