The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Minimum redundancy feature selection from microarray gene expression data
Abstract
Selecting a small subset of genes 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. Feature sets obtained through the minimum redundancy - maximum relevance framework represent broader spectrum of characteristics of phenotypes than those obtained through standard ranking methods; they are more robust, generalize well to unseen data, and lead to significantly improved classifications in extensive experiments on 5 gene expressions data sets.
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The Nature of Statistical Learning Theory
Vapnik, V. N. (1995) · doi:10.1007/978-1-4757-2440-0 · Cited by 39,138
Cited by
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Marcilio, W. E., & Eler, D. M. (2020) · 2020 33rd SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI) · doi:10.1109/sibgrapi51738.2020.00053 · Cited by 504
Wei, L., Xing, P., Zeng, J., Chen, J., Su, R., & Guo, F. (2017) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2017.03.001 · Cited by 242
Massively parallel feature selection: an approach based on variance preservation
Zhao, Z., Zhang, R., Cox, J., Duling, D., & Sarle, W. (2013) · Machine Learning · doi:10.1007/s10994-013-5373-4 · Cited by 85
Metadata
- Authors
- Ding, C., & Peng, H.
- Venue
- Computational Systems Bioinformatics. CSB2003. Proceedings of the 2003 IEEE Bioinformatics Conference. CSB2003
- Publisher
- IEEE Comput. Soc
- Published
- 2004-03-30
- Pages
- 523-528
- OpenAlex
- W2153678774
- Cited by
- 506
- References
- 1
- FWCI
- 31.10
- Type
- conference paper
- Language
- en
- Open access
- closed
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