Table 5.

The detailed parameter settings of preset classifiers.

Preset Classifier	Description	Parameter	Value
FDT	Fine Decision Tree	Maximum number of splits	100
		Split criterion	Gini’s diversity index
CDT	Coarse Decision Tree	Maximum number of splits	100
		Split criterion	Gini’s diversity index
LDA	Linear Discrimenant Analaysis	Discriminant type	linear
LR	Logistic Regression	-	-
GNB	Gaussian Naïve Bayes	Distribution name	Gaussian
KNB	Kernel Naïve Bayes	Distribution name	Kernel
		Kernel type	Gaussian
LSVM	Linear Support Vector Machine	Kernel function	Linear
		Kernel scale	Automatic
		Box contraint level	1
		standardize data	TRUE
MGSVM	Medium Gaussian SVM	Kernel function	Gaussian
		Kernel scale	5.6
		Box contraint level	1
		Standardize data	TRUE
CGSVM	Coarse Gaussian SVM	Kernel function	Gaussian
		Kernel scale	22
		Box contraint level	1
		Standardize data	TRUE
CKNN	Cosine K-Nearest Neighbor	Number of neighbors	10
		Distance metric	cosine
		Distance weight	equal
		Standardize data	TRUE
WKNN	Weighted kNN	Number of neighbors	10
		Distance metric	Euclidean
		Distance weight	Squared Inverse
		Standardize data	TRUE
Ensemble	Subspace Discriminant	Ensemble method	Subspace
		Learner type	Discriminant
		Number of learners	30
		Subspace dimension	16