show very different gene expression patterns [@Gatza2010]. Expression patterns predictive of outcome might vary between subtypes, which typically leads to problems when training classifiers on gene expression data derived from breast tumors. If this is not explicitly taken into account during classifier training it could result in poor performance and unstable classification, as the selected genes may depend on the composition of the training set. In this section we control the heterogeneity in both the training and test sets by only selecting the relatively homogeneous ER positive breast cancer sub-population. Since the training sets become too small in the paired setting if we only select the ER positive cases, we followed the merged setting outlined above. More specifically, we created a test set consisting of all ER positive cases of a single data set and a training set by pooling all ER positive cases from the remaining data sets. We repeated this procedure across the six data sets and thus obtained six AUC values per classifier. Figure \[fig:ER\_pos\] depicts the results for the CV-optimized feature sets and the top 50 features. As before, the classifiers employing a fixed number of features perform similar to classifiers based on a feature set 