Figure 2
From: Multiclass classification of microarray data with repeated measurements: application to cancer
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). Results of ρ0 < 0.6 are shown in Figure S1 on [30]. Optimal parameters are inferred from the cross-validation data in (b).