Table 2.

Model configurations for MLP and CNN

	Synthetic	CBH	CSS	HMP	CS	FS	FSH	IBD	PDX
MLP	(256, 256)	(1024, 512)	(512, 256)	(512, 256)	(512, 512)	(512, 512)	(512, 256)	(512, 256, 128)	(512, 256, 128)
CNN	Conv1D(8, 3) → Dropout → ReLu → MaxPool1D(2) → Conv1D(8, 3) → ReLu → MaxPool1D(2) → FC

Number in the round bracket represents the number of hidden units. Conv1D is the one-dimensional convolution layer. ReLu is the non-linear rectifier layer. MaxPool1D represents the one-dimensional max pooling layer. Dropout and FC represent dropout and fully connected layers, respectively. Details of each dataset are described in Table 1