Figure 5.

Improvements in the area under the ROC curves by increasing the number of training samples. Except for Bolasso on colon dataset, the average performance increases as more training samples are provided. While FeaLect and lars converge to a common asymptotic performance on lymphoma dataset, FeaLect is consistently superior to pure lars on colon dataset because the number of training samples is very limited. Table 1 presents similar superiority for other datasets with relatively low instances.

Zare et al. BMC Genomics 2013 14(Suppl 1):S14   doi:10.1186/1471-2164-14-S1-S14