Comparison Of Various Kernels Of Support Vector Machine
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2015, Vol 3, Issue 7
Abstract
As we know, classification plays an important role in every field. Support vector machine is the popular algorithm for classification and prediction. For classification and prediction by support vector machine, LIBSVM is being used as a tool. Support vector machine classifies the data points using straight line. Some datasets are impossible to separate by straight line. To cope with this problem kernel function is used. The central idea of kernel function is to project points up in a higher dimensional space hoping that separability of data would improve. There are various kernels in the LIBSVM package. In this paper, Support Vector Machine (SVM) is evaluated as classifier with four different kernels namely linear kernel, polynomial kernel, radial basis function kernel and sigmoid kernel. Several datasets are being experimented to find out the performance of various kernels of support vector machines. Based on the best performance result, linear kernel is capable of classifying datasets accurately with the average accuracy 88.20 % of correct classification and faster with 4.078 sec of prediction time. Radial basis function Kernel is capable of taking less training time compared to other kernels that is 4.92675 sec.
Authors and Affiliations
Supriya Pahwa, Deepak Sinwar
Keywords
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