Kernel evolution for support vector classification

Support vector machines (SVMs) have been used in a variety of classification tasks. SVMs undoubtedly are one of the most effective classifiers in several data mining applications. Determination of a kernel function and related parameters has been a bottleneck for this group of classifiers. In this paper a novel approach is proposed to use genetic programming (GP) to design domain-specific and optimal kernel functions for support vector classification (SVC) which automatically adjusts the parameters. Complex low dimensional mapping function is evolved using GP to construct an optimal linear and Gaussian kernel functions in new feature space. By using the principled kernel closure properties, these basic kernels are then used to evolve more optimal kernels. To evaluate the proposed method, benchmark datasets from UCI are applied. The result indicates that for some cases the proposed methods can find a more optimal solution than evolving known kernels.