Table 2.

Classifier optimized hyperparameters and variation range.

Classifier	Hyperparameter	Variation Range
k-nearest neighbour (k-NN)	Distance (DD)	{cityblock, chebychev, correlation, cosine, euclidean, hamming, jaccard, mahalanobis, minkowski,spearman}
	DistanceWeight (DW)	{equal, inverse, squaredinverse}
	Exponent (E)	[0.5, 3]
	NumNeighbors (NN)	[1, 5]
Support vector machine (SVM)	BoxConstraint (BC)	log-scaled in the range [1e-3,1e3]
	KernelFunction (KF)	{gaussian, linear, polynomial}
	KernelScale (KS)	log-scaled in the range [1e-3,1e3]
	PolynomialOrder (PO)	{2,3,4}
Artificial neural network (ANN)	Activation Function (AF)	{relu, sigmoid, tanh}
	Hidden layer nr. of neurons (HLN)	[25, 200]
Linear discriminant analysis (LDA)	Gamma (G)	[0,1]
	Delta (D)	log-scaled in the range [1e-6,1e3]
	DiscrimType (DT)	{linear, quadratic, diagLinear,}
		{diagQuadratic, pseudoLinear, pseudoQuadratic}
Random forest (RF)	Depth (D)	[5,20]
	Number of trees (NT)	[15,100]
	Maximum depth of the tree	[5,30]
Logistic regression (LR)	Penalty (P)	{L2, elastic net}
	Inverse of regularization strength (C)	[0.25, 1.0]