Table 3 Parameters used with the logistic regression classifier for the breast cancer application
From: Robust identification of molecular phenotypes using semi-supervised learning
Method | Parameter | Value(s) |
|---|---|---|
Bagged logistic regression (applied to the breast cancer data set) | Number of features used in training (selected by t-test) | 100 |
Number of features used in each dropout iteration | 1 | |
Number of dropout iterations (in the boosting step) | 5000 | |
Number of training / test realizations | 101 | |
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 (converged at iteration 8) |