Complexity Digest 2008.21 - 17
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The Ant Colony Algorithm For Feature Selection In High-Dimension Gene Expression Data For Disease Classification, Math. Medi. & Biol.
Excerpt: The use of gene expression data to diagnose complex diseases represents an exciting area of medicine; however, such data sets are often noisy, requiring the selection of feature subsets to obtain maximum classification accuracy. Due to the high dimensions of many expression data sets, filter-based methods are commonly used, but often yield inconsistent results. Optimization algorithms can outperform filter methods, but often require preselection of features to achieve good results. To address the problems of many commonly used feature selection methods, the ant colony algorithm (ACA) is proposed for use on data sets with large numbers of features. (...)
- Source: The Ant Colony Algorithm For Feature Selection In High-Dimension Gene Expression Data For Disease Classification
[ http://imammb.oxfordjournals.org/cgi/content/abstract/24/4/413-a ], K. R. Robbins, W. Zhang, J. K. Bertrand, R. Rekaya - krobbin1uga.edu, DOI: 10.1093/imammb/dqn001, Mathematical Medicine and Biology, Dec. 2007, on line 2008/02/22
- Contributed by Pritha Das - prithadas01yahoo.com