IMPROVING THE CLASSIFICATION ACCURACY USING SUPPORT VECTOR MACHINES (SVMS) WITH NEW KERNEL

Abstract

In this paper, we introduce a new kernel function called polynomial radial basis function (PRBF) that could improve the classification accuracy of support vector machines (SVMs). The proposed kernel function combines both Gauss (RBF) and Polynomial (POLY) kernels and is stated in general form. It is shown that the proposed kernel converges faster than the Gauss and Polynomial kernels. The accuracy of the proposed algorithm is compared to algorithms based on both Gaussian and polynomial kernels by application to a variety of non-separable data sets with several attributes. We noted that the proposed kernel gives good classification accuracy in nearly all the data sets, especially those of high dimensions.

Keywords: Classification problem, SVMs, kernel functions.