Hybrid SVM kernels for protein secondary structure prediction

The Support Vector Machine is a powerful methodology for solving problems in nonlinear classification, function estimation and density estimation. When data are not linearly separable, data are mapped to a high dimensional future space using a nonlinear function which can be computed through a positive definite kernel in the input space. Using a suitable kernel function for a particular problem and input data can change the prediction results remarkably and improve the accuracy. The goal of this work is to find the best kernel functions that can be applied to different types of data and problems. In this paper, we propose two hybrid kernels SVMSM+RBF and SVMEDIT+RBF. SVMSM+RBF is designed by combining the best performed RBF kernel with substitution matrix (SM) based kernel developed by Vanschoenwinkel and Manderick. SVMEDIT+RBF kernel combines the RBF kernel and the edit kernel devised by Li and Jiang. We tested these two hybrid kernels on one of the widely studied problems in bioinformatics which is the protein secondary structure prediction problem. For the protein secondary structure problem, our results were 91% accuracy on H/E binary classifier.