Abstract
In the paper, three conceptually simple but computer-intensive versions of an approach to selecting informative genes for classification are proposed. All of them rely on multiple construction of a tree classifier for many training sets randomly chosen from the original sample set, where samples in each training set consist of only a fraction of all of the genes. It is argued that the resulting ranking of genes can then be used to advantage for classification via a classifier of any type.
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Dramiński, M., Koronacki, J., Komorowski, J. (2005). A study on Monte Carlo Gene Screening. In: Kłopotek, M.A., Wierzchoń, S.T., Trojanowski, K. (eds) Intelligent Information Processing and Web Mining. Advances in Soft Computing, vol 31. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-32392-9_36
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DOI: https://doi.org/10.1007/3-540-32392-9_36
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