TABLE VI.

Comparison of three different feature selection and classification methods.

Baseline		G-mean (%)	SEN (%)	SPEC (%)	AUC	#F
TCFA	SVM RFE+SVM	85.9	92.7	79.6	0.87	16
	mRMR+SVM	74.3	76.4	72.2	0.77	12
	SVM RFE+MLP	83.1	85.5	80.7	0.88	15
	mRMR+MLP	71.9	76.4	67.7	0.75	27
	RF	77.9	70.9	85.6	0.82	16
ThCFA	SVM RFE+SVM	81.7	91.7	72.8	0.84	13
	mRMR+SVM	66.3	58.3	75.4	0.77	14
	SVM RFE+MLP	79.6	83.3	76.0	0.85	17
	mRMR+MLP	75.4	88.9	64.0	0.82	20
	RF	68.0	58.3	79.2	0.77	7
nonFA	SVM RFE+SVM	77.0	63.6	93.3	0.92	37
	mRMR+SVM	78.7	75.8	81.8	0.87	12
	SVM RFE+MLP	83.7	97.0	72.3	0.87	12
	mRMR+MLP	81.9	90.9	73.9	0.88	6
	RF	78.7	75.8	81.7	0.89	7
PB≥70%	SVM RFE+SVM	80.8	77.7	84.0	0.88	26
	mRMR+SVM	70.3	75.0	66.0	0.81	16
	SVM RFE+MLP	74.8	74.1	75.5	0.83	10
	mRMR+MLP	72.4	79.5	66.0	0.82	12
	RF	78.9	77.1	80.8	0.87	9
PB<70%	SVM RFE+SVM	85.6	85.3	85.9	0.93	25
	mRMR+SVM	76.0	70.5	82.0	0.87	37
	SVM RFE+MLP	82.4	88.5	76.7	0.89	22
	mRMR+MLP	76.2	72.1	80.5	0.88	8
	RF	78.6	75.4	81.9	0.89	10
MLA≤4mm2	SVM RFE+SVM	81.6	86.4	77.0	0.86	27
	mRMR+SVM	73.5	75.7	71.4	0.81	17
	SVM RFE+MLP	77.9	81.4	74.6	0.86	8
	mRMR+MLP	76.3	75.7	77.0	0.81	21
	RF	76.0	77.4	74.6	0.83	11
MLA>4mm2	SVM RFE+SVM	80.1	83.3	77.0	0.86	18
	mRMR+SVM	71.4	77.8	65.6	0.78	3
	SVM RFE+MLP	74.6	74.4	74.8	0.83	17
	mRMR+MLP	72.4	72.2	72.6	0.77	2
	RF	73.3	70.0	76.8	0.80	20
Average	SVM RFE+SVM	81.8	83.0	81.4	0.88	23
	mRMR+SVM	72.9	72.8	73.5	0.81	16
	SVM RFE+MLP	79.4	83.5	75.8	0.86	14
	mRMR+MLP	75.2	79.4	71.7	0.82	14
	RF	75.9	72.1	80.1	0.84	11

mRMR (minimal-redundancy-maximal-relevance): based on mutual information.

MLP (multilayer perceptron): 1 hidden layer with the number of neurons = (# features + # classes) / 2, epochs = 500, and learning rate = 0.01.

RF (random forests): 100 trees with the size of features for node splitting $=\sqrt{\#\mathit{features}}$