Loading...
Please wait, while we are loading the content...
Similar Documents
Minimum Redundancy Feature Selection from Microarray Gene Expression Data (2003)
| Content Provider | CiteSeerX |
|---|---|
| Author | Ding, Chris Peng, Hanchuan |
| Description | Motivation. How to selecting a small subset out of the thousands of genes in microarray data is important for accurate classification of phenotypes. Widely used methods typically rank genes according to their differential expressions among phenotypes and pick the top-ranked genes. We observe that feature sets so obtained have certain redundancy and study methods to minimize it. Results. We propose a minimum redundancy – maximum relevance (MRMR) feature selection framework. Genes selected via MRMR provide a more balanced coverage of the space and capture broader characteristics of phenotypes. They lead to significantly improved class predictions in extensive experiments on 5 gene expression data sets: NCI, |
| File Format | |
| Language | English |
| Publisher Date | 2003-01-01 |
| Publisher Institution | J Bioinform Comput Biol |
| Access Restriction | Open |
| Subject Keyword | Microarray Gene Expression Data Microarray Data Accurate Classification Certain Redundancy Balanced Coverage Small Subset Feature Selection Framework Differential Expression Extensive Experiment Minimum Redundancy Feature Selection Top-ranked Gene Minimum Redundancy Maximum Relevance Class Prediction Gene Expression Data Set Study Method |
| Content Type | Text |
| Resource Type | Article |
