Abstract. Support Vector Machines (SVMs) are a popular data classification method with many
diverse applications. The SVMs performance depends on choice a suitable kernel function for a given
problem. Using an appropriate kernel; the data are transform into a space with higher dimension in
which they are separable by an hyperplane. A major challenges of SVMs are how to select an
appropriate kernel and how to find near optimal values of its parameters. Usually a single kernel is
used by most studies, but the real world applications may required a combination of multiple kernels.
In this paper, a new method called, weighted powered kernels for data classification is proposed. The
proposed method combined three kernels to produce a new combined kernel (WPK). The method used
Scatter Search approach to find near optimal values of weights, alphas and kernels parameters which
associated with each kernel. To evaluate the performance of the proposed method, 11 benchmark are
used. Experiments and comparisons prove that the method given acceptable outcomes and has a
competitive performance relative to a single kernel and some other published methods