A) Average error of all classifiers and descriptors, B) ROC curves for Classification using AdaBoost and Bagging with intensity and CMYK&Hb&Lb&HSV&Lab colour model combination.
BackgroundTypically, a two-phase (double) sampling strategy is employed when classifications are subject to error and there is a gold standard (perfect) classifier available. Two-phase sampling involv
Six PDM data sets (T = 10, 50 and 100 generations and J = 4 and 8 populations) and 3 MDM data sets (FST = 0.05, 0.1 and 0.15 and J = 8 populations) were analyzed. Two ROC curves per analyzed data set
AUC results for low p/n data. Low p/n results for prediction accuracy using AUC as the performance metric for non-cross-validation results, 10-fold cross-validation and stratified 10-fold cross-valida