Combined Kernel Function Approach in SVM for Diagnosis of Cancer

The problem of determining optimal decision model is a difficult combinatorial task in the fields of pattern classification, machine learning, and especially bioinformatics. Recently, support vector machine (SVM) has shown a higher performance than conventional learning methods in many applications. This paper proposes a new kernel function for support vector machine (SVM) and its learning method that results in fast convergence and good classification performance. The new kernel function is created by combining a set of kernel functions. A new learning method based on evolution algorithm (EA) is proposed to obtain the optimal decision model consisting of an optimal set of features as well as an optimal set of the parameters for combined kernel function. The experiments on clinical datasets such as stomach cancer, colon cancer, and leukemia datasets data sets indicates that the combined kernel function shows higher and more stable classification performance than other kernel functions.