Weighted Kernel and it's Learning Method for Cancer Diagnosis System
Gyoo-Seok Choi, Jong‐Jin Park, Byoung-Chan Jeon, Inkyu Park, Ihn-Seok Ahn, Ha-Nam Nguyen · The Journal of the Institute of Webcasting, Internet and Telecommunication · 2009
One of the most important problems in bioinformatics is how to extract the useful information from a huge amount of data, and make a decision in diagnosis, prognosis, and medical treatment 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 indicate that our weighted kernel function results in higher and more stable classification performance than other kernel functions.