Breast Cancer Diagnosis via Supp ort Vector Machines

Author

Yi Wang ; Fuyong Wan

Author_Institution

Dept. of Math., East China Normal Univ., Shanghai, China

fYear

2006

fDate

7-11 Aug. 2006

Firstpage

1853

Lastpage

1856

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Control Conference, 2006. CCC 2006. Chinese

Conference_Location

Harbin

Print_ISBN

7-81077-802-1

Type

conf

DOI

10.1109/CHICC.2006.280871

Filename

4060419

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3213718