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Table 3 Pseudocode for a Monte Carlo wrapper-based feature selection algorithm

From: Win percentage: a novel measure for assessing the suitability of machine classifiers for biological problems

MCW(S, N)
   1. xout = -∞
   2. For i = 1 to N
S i = randomSubset(S)
(x i , C i ) = performance(S i )
If x i >xout,
Sout = S i , xout = x i , cout = randomElement(C i )
   3. output Sout, xout, cout
  1. The input, S, is the set of all features, and N is the total number of feature subsets to draw randomly. The variable x i is the performance of the top classifier for subset S i , and C i is the label of the top classifier. Sout, xout, and cout, return the top performing feature set, top estimated performance, and top classifier, respectively.