Name
Title | ||
|---|---|---|
Microarray Gene Expression Classification Based on Supervised Learning and Similarity Measures |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1109/ICSMC.2006.385116 | SMC |
Keywords | Field | DocType |
supervised learning,learning (artificial intelligence),genetics,t-test gene selection method,pattern classification,microarray gene expression data classification,svm-rfe gene selection method,biology computing,molecular biophysics,similarity measures,recursive feature addition,feature extraction,support vector machines,learning artificial intelligence,gene expression,gene selection | Gene selection,Similarity measure,Pattern recognition,Computer science,Support vector machine,Feature extraction,Supervised learning,Artificial intelligence,Microarray gene expression,Machine learning | Conference |
Volume | ISSN | ISBN |
6 | 1062-922X | 1-4244-0100-3 |
Citations | PageRank | References |
1 | 0.38 | 3 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qingzhong Liu | 1 | 588 | 44.77 |
| Andrew H. Sung | 2 | 1034 | 84.10 |
| Jianyun Xu | 3 | 187 | 11.13 |
| Jianzhong Liu | 4 | 1 | 0.38 |
| Zhongxue Chen | 5 | 244 | 15.77 |