From: Robust identification of molecular phenotypes using semi-supervised learning
Method | Parameter | Value(s) |
---|---|---|
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 |