Table 2:

Classification results of different approaches summarized by Accuracy, specificity (SPE), sensitivity (SEN) and area under the ROC curve (AUC). The best score in each category is in bold. Methods are marked with †, if they were significantly worse than the proposed approach (p < 0.01 according to Delong’s Test [59]). Methods marked with ‡ are significantly better than chance (p < 0.01 according to the Fisher exact test [52]).

	Method	Accuracy (%)	SPE	SEN	AUC
Proposed	ℓ2,1-ℓ2-reg‡	82.3	0.82	0.84	0.87
Chained (Baseline)	ℓ1-ℓ2-reg‡	81.9	0.82	0.79	0.86
	Avg ℓ1-ℓ2-reg‡	79.7	0.82	0.77	0.85
	ℓ2-ℓ2-reg†‡	73.1	0.74	0.73	0.76
	ℓ2,1ℓ1-reg†‡	72.5	0.72	0.73	0.76
Single Step Regularization	ℓ1-reg†‡	70.3	0.70	0.70	0.75
	ℓ2,1-reg†‡	69.7	0.70	0.68	0.73
	ℓ2-reg†‡	68.7	0.64	0.70	0.71
Conventional Methods	SFS [20]+SVM†‡	69.9	0.69	0.70	0.73
	elastic-net [24]+SVM†‡	65.1	0.64	0.64	0.69
	t-test [20]+SVM†	59.1	0.61	0.56	0.65
	mRMR [58]+SVM†	59.6	0.56	0.61	0.64
	SparseSVM [57]†	57.9	0.55	0.60	0.64
	SVM†	56.7	0.57	0.56	0.60