Tissue classification algorithm based on NPE and LS-SVM

Author

Sun, Xia ; Wang, Zi-qiang

Author_Institution

Sch. of Inf. Sci. & Eng., Henan Univ. of Technol., Zhengzhou, China

Volume

1

fYear

2009

fDate

14-16 Aug. 2009

Firstpage

1146

Lastpage

1149

Abstract

Tissue classification with gene expression data is of great importance in medical diagnosis of diseases. To efficiently cope with this problem, a novel tissue classification algorithm based on neighborhood preserving embedding (NPE) and least square SVM (LS-SVM) is proposed in this paper. The experimental results on four well-known gene expression data sets demonstrate the effectiveness of the proposed algorithm.

Keywords

biological tissues; diseases; least squares approximations; medical signal processing; signal classification; support vector machines; LS-SVM; NPE; disease; gene expression data; least square SVM; medical diagnosis; neighborhood preserving embedding method; support vector machine; tissue classification algorithm; Algorithm design and analysis; Classification algorithms; Diseases; Gene expression; Least squares methods; Linear discriminant analysis; Medical diagnosis; Principal component analysis; Support vector machine classification; Support vector machines;

fLanguage

English

Publisher

ieee

Conference_Titel

IT in Medicine & Education, 2009. ITIME '09. IEEE International Symposium on

Conference_Location

Jinan

Print_ISBN

978-1-4244-3928-7

Electronic_ISBN

978-1-4244-3930-0

Type

conf

DOI

10.1109/ITIME.2009.5236251

Filename

5236251