Fig. 5
Results of five-fold cross-validation and in-center validation in real world. A Training curve of AlexNet model extracting the delta-DL features (ICV accuracy vs. ICV loss during the training process). B A representative confusion matrix of a near-average classification result by dual-delta machine learning model. C T-SNE unsupervised clustering of features. D Five-fold cross-validation ROC curves and their AUC values. E In-center validation ROC curves and their AUC values. F AUC values and the feature numbers for the combinations of feature selection algorithms and their optimal classification models, where the LASSO cross-validation plot and LASSO trajectory plots of variables (green vertical lines represent the number of features corresponding to MSEmin), and the ranked feature weights by ReliefF (pie charts show the compositions of essential feature sets selected by LASSO and ReliefF) are given