Comparative Analysis on Effect of Different SVM Kernel Functions for Classification

Besides linear classification, Support Vector Machine (SVM) is proficient in non-linear classification by deploying kernel tricks that implicitly maps and transform input features to high dimensional feature space. Kernel-SVM, can be utilized to secure progressively complex connections on datasets with no push to do changes all alone. In this paper, 5 different SVM kernel functions are implemented on 4 datasets, viz., IRIS, Breast Cancer Wisconsin (diagnostic), Mushroom and Letter Recognition Dataset. The five kernel functions considered in this paper are: Linear kernel, Gaussian Radial Basis Function (RBF) kernel, Laplacian kernel, Polynomial kernel and Sigmoid kernel. Our goal is to locate the best non-linear kernel. The outcomes show that the precision of expectation for Laplacian kernel is most extreme with a forecast scope of (max 100%, min 97.53%) and least for the sigmoid kernel with a forecast scope of (max 100%, min 47.28%).