Title :
Breast Cancer Diagnosis via Supp ort Vector Machines
Author :
Yi Wang ; Fuyong Wan
Author_Institution :
Dept. of Math., East China Normal Univ., Shanghai, China
Abstract :
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.
Keywords :
cancer; patient diagnosis; support vector machines; breast cancer diagnosis; cross-entropy filter; fast searching method; generalization bound; margin-based bound; nonsymmetrical C-SVM; support vector machines; Accuracy; Breast cancer; Costs; Decision trees; Diseases; Filters; Linear programming; Mathematics; Support vector machine classification; Support vector machines; Breast Cancer Diagnosis; Cross-entropy Filter; Generalization Bound; Non-symmetrical C-SVM;
Conference_Titel :
Control Conference, 2006. CCC 2006. Chinese
Conference_Location :
Harbin
Print_ISBN :
7-81077-802-1
DOI :
10.1109/CHICC.2006.280871
