Breast Cancer Diagnosis via Supp ort Vector Machines
Yi Wang, Wan Fuyong · 2006
This paper describes the application of SVM to breast cancer diagnosis, which has shown good generalization. We take use of non-symmetrical C-SVM to solve the problem of unbalanced training examples. In order to gain a fast searching method for parameters of the model, a margin-based bound on generalization is more effective than traditional k-fold cross-validation. After feature subset selection by a cross-entry filter, we even gained a perfect prediction accuracy.