Details

Autor(en) / Beteiligte

• Qingzhong Liu
• Sung, A.H.
• Jianyun Xu
• Jianzhong Liu
• Zhongxue Chen

Titel

Microarray Gene Expression Classification Based on Supervised Learning and Similarity Measures

Ist Teil von

• 2006 IEEE International Conference on Systems, Man and Cybernetics, 2006, Vol.6, p.5094-5099

Ort / Verlag

IEEE

Erscheinungsjahr

2006

Link zum Volltext

• IEEE_Xplore

Quelle

IEL

Beschreibungen/Notizen

• Microarray gene expression data has high dimension and small samples, the gene selection is very important to the classification accuracy. In this paper, we present a scheme of recursive feature addition for microarray gene expression classification based on supervised learning and the similarity measure between chosen genes and candidates. In comparison with the well-known gene selection methods of T-TEST and SVM-RFE using different classifiers, our method, on the average, performs the best regarding the classification accuracy under different feature dimensions, the mean test accuracy and the highest test accuracy under the highest train accuracy, and the highest test accuracy in the experiments.