MINIMUM REDUNDANCY FEATURE SELECTION FROM MICROARRAY GENE EXPRESSION DATA
Lawrence Berkeley National Laboratory · University of California, Berkeley
Abstract
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. 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 6 gene expression data sets: NCI, Lymphoma, Lung, Child…
Citation impact
- FWCI
- 13.27
- Percentile
- 100%
- References
- 23
Authors
2- CDChris DingCorresponding
Lawrence Berkeley National Laboratory, University of California, Berkeley
- HPHANCHUAN PENG
Lawrence Berkeley National Laboratory, University of California, Berkeley
Topics & keywords
- Feature selection
- Minimum redundancy feature selection
- Redundancy (engineering)
- Support vector machine
- Bayes' theorem
- Gene
- Microarray analysis techniques
- Computational biology
- Reduced inequalities