Figure 2.

A corrected figure showing the prediction accuracy on the multiple tumor data using the EWUSC algorithm over the range of Δ from 0 to 20. The percentage of classification errors is plotted against Δ on (a) the full training set (96 samples) and (c) the test set (27 samples). In (b) the average percentage of errors is plotted against Δ on the cross-validation data over five random runs of fourfold cross-validation. In (d), the number of relevant genes is plotted against Δ. Different colors are used to specify different correlation thresholds (ρ₀ = 0.6, 0.7, 0.8, 0.9 or 1). Optimal parameters are inferred from the cross-validation data in (b).