Optimizing Weighted Kernel Function for Support Vector Machine by Genetic Algorithm

Abstract

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 better performance than conventional learning methods in many applications. This paper proposes a weighted kernel function for support vector machine and its learning method with a fast convergence and a good classification performance. We defined the weighted kernel function as the weighted sum of a set of different types of basis kernel functions such as neural, radial, and polynomial kernels, which are trained by a learning method based on genetic algorithm. The weights of basis kernel functions in proposed kernel are determined in learning phase and used as the parameters in the decision model in classification phase. The experiments on several clinical datasets such as colon cancer, leukemia cancer, and lung cancer datasets indicate that our weighted kernel function results in higher and more stable classification performance than other kernel functions. Our method also has comparable and sometimes better classification performance than other classification methods for certain applications.