Name
Abstract | ||
|---|---|---|
We have investigated the use of boosting techniques for feature selection for microarray data analysis. We propose a novel algorithm for feature selection and have tested it on three datasets. The results clearly show that our boosting technique for feature selection outperformed the Wilcoxon-Mann-Whitney U-test commonly used in microarray data analysis, and produced more accurate boosting ensembles when they were constructed with the features selected by our technique. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/IJCNN.2008.4634156 | 2008 IEEE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-8 |
Keywords | Field | DocType |
feature selection,artificial neural networks,data analysis,accuracy,wilcoxon mann whitney,cancer,learning artificial intelligence,signal to noise ratio,classification algorithms,testing,gene expression,microarray data analysis,feature extraction,boosting,dna,machine learning | Pattern recognition,Feature selection,Computer science,Wilcoxon-Mann-Whitney U Test,Feature extraction,Microarray analysis techniques,Artificial intelligence,Boosting (machine learning),Statistical classification,Artificial neural network,Machine learning | Conference |
ISSN | Citations | PageRank |
2161-4393 | 0 | 0.34 |
References | Authors | |
9 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Geoffrey R. Guile | 1 | 8 | 1.68 |
| Wenjia Wang | 2 | 57 | 9.12 |