A Comparison of SVM Kernel Functions for Breast Cancer Detection

Support vector machines outperform other classification methods for breast cancer detection. However the performance of SVM is greatly affected by the choice of a kernel function among other factors. This article presents a comparative study of different kernel functions for breast cancer detection. The focus is on classification using SVM with different kernel functions. The comparison with neural network based method using MLP is also given. Furthermore, we examine the affect of selecting feature subsets before applying classification with different kernels. For features subset selection we used genetic algorithm. The evaluation is based on 5 X 2 cross validation.