Fig. 1
From: Robust identification of molecular phenotypes using semi-supervised learning
Performance of the iterative refinement approach on synthetic data with α = 1 for NA = NB = 60. For each development set realization the IRA was applied. At each refinement iteration, the classifiers were applied to their development set realization and the independent validation set. Concordance of classifier-derived phenotype with true phenotype is shown for (a) the ten development set realizations, and (b) the validation set, for all ten development set realizations as a function of refinement iteration. The difference between the hazard ratio for classifier-derived phenotypes and the hazard ratio for phenotype A vs phenotype B in the development sets, ∆HR, is shown in (c) as a function of refinement iteration. The hazard ratios for classifier-derived phenotypes in the validation set as a function of refinement iteration are shown in (d). The value of the hazard ratio in the validation set for phenotype A vs B (HR = 1.63) is indicated by the dashed line. The crossed open circle indicates lack of convergence after ten refinement iterations