Average AUC(ROC) and AUC(PvsR) for the five networks of three different sizes from the DREAM3 In Silico benchmarks, calculated through the confidence matrices , , and . The best value of each row is b
This table contrasts the classification results obtained through generative embedding with those afforded by three conventional methods. As described in the main text, the underlying dataset serves il
Results for different basic classifiers (mean±SD) by using varied numbers of supplementary training data, trained and tested in 10-fold cross-validation on the plant dataset.
Recently, hybrid feature selection methods have demonstrated excellent performance on high-dimensional data, but many of these methods tend to yield relatively homogeneous feature subsets. To address
