Performance evaluation of kernel functions based on grid search for support vector regression

Abstract

The support vector machine (SVM) has recently been successfully used for classification and regression problems. As the core of SVM, the kernel function is used to map the input vectors into a higher dimensional feature space to realize the non-linear algorithm for original space. The selection of kernel function and corresponding parameters directly affects the learning ability and generalization ability of the support vector regression (SVR). This paper compares the performance of different kernel functions on the energy efficiency data obtained from the UCI repository. The kernel functions used are as follows: Linear, Gaussian, Polynomial, Sigmoid, Inv. Multiquadric and the Semi Local kernel. The influence of parameters on the mean square error of cross validation (MSECV) for each kernel is shown in the paper. The combination of SVR parameters and kernel parameter with minimum MSECV obtained by grid search method based on cross-validation is chosen for SVR model building. The experiment results based on the dataset showed that the Semi Local kernel has the best predictive capability. The mean-squared error of prediction (MSEP) of the Semi Local kernel was 97.13%, 8.26%, 0.96%, 99.95%, 27.36% lower than the value of the Linear, Gaussian, Polynomial, Sigmoid and Inv. Multiquadric kernel.