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Table 2 Parameter tuning and selection method used in this study

From: Gene masking - a technique to improve accuracy for cancer classification with high dimensionality in microarray data

Parameter tuning and selection
Let S be the set of training samples
Let CR be the crossover rate and MR be the mutation rate
Let k be the number of cross validation folds, where k = 5 is fixed
Let α be the Accuracy to Elimination Ratio
Define the GA parameters apart from CR and MR as those highlighted in Table 1
Define α to belong to the set (0, 0.1, 0.2, …, 0.9, 1]
Define CR to belong to the set (0.5, 0.55, 0.6, …, 0.95, 1]
Define MR to belong to the set [0, 0.05, 0.1, …, 0.45, 0.5)
For each combination of { α, CR, MR}:
- Perform k-fold cross validation using the classifier and gene masking on the set of samples S
- Report the results obtained by the best performing gene mask
- Repeat for 10 iterations
Select the best performing combination of { α, CR, MR} for testing and reporting