Figure 4.

Senstivity of classifiers to normalization and machine-learning parameters. Decision rules trained and validated on breast cancer dataset GSE3494 using supervised feature selection. Row 1: Expression values obtained using different pre-processing algorithms. Row 2: Different univariate feature selection methods. Row 3: Different classification schemes. Row 4: Different mode of partition into training and test data. E is the mean 1-AUC for the corresponding set of ROC curves, calculated as described in the Methods section. Error bars are empirical 95% CIs.