A study on Monte Carlo Gene Screening
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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