Fig. 13

Precision-recall curves of CNN models of increasing number of layers on the three datasets of increasing size. The higher the curve, the better performance with a horizontal dashed line to represent random prediction. An ideal precision-recall curve would cross the (1,1) point. The splice data has three panels since precision-recall curves assume binary classification and the splice dataset has three classes (0, 1, 2). Each panel corresponds to prediction one class vs the other two combined. Nguyen2D corresponds to the original CNN model in [30], while Nguyen2D+1D corresponds to the same model with an extra 1D convolutional layer. Similarly, Zeng2 corresponds to the original model in [31] which has two 2D convolutional layers while Zeng3 and Zeng4 correspond to models with three and four 2D convolutional layers respectively. The CNN-Nguyen models outperform the other models across datasets