Table 2 Parameters used with the dropout regularized classifier for the synthetic data investigation

DRC classifier (applied to the synthetic data)

k (kNN sub-classifiers)

9

Subsets of features used in the sub-classifiers

Singles

Sub-classifier filtering criteria

Survival HR between the two classification groups

Sub-classifier filtering range applied to the training set

[1.5; 10.0]

Number of dropout iterations (in the boosting step)

15,000

Number of sub-classifiers kept in each dropout iteration

4

Number of training / test realizations

325

Number of samples included in the training subset, for each class

\( 2/3\times {N}_{\mathsf{S}} \), where \( {N}_{\mathsf{S}} \) is the number of samples in the smaller class. Remainder samples assigned to the test subset

Maximum number of refinement iterations

10