Table 6. Testing performance of dataset II.

Type		SI		AA		Decision fusion				Feature fusion
Classifier		SVM	NN	SVM	NN	SVM		NN		SVM	NN
						SVM	NN	SVM	NN
Alpha						0.73	0.69	0.80	0.76
Hamming-loss		0.103	0.119	0.064	0.063	0.045	0.054	0.054	0.063	0.083	0.098
Accuracy		0.790	0.800	0.883	0.885	0.906	0.898	0.879	0.889	0.823	0.831
Precision		0.857	0.829	0.901	0.906	0.942	0.918	0.947	0.907	0.889	0.856
Recall		0.825	0.831	0.908	0.908	0.924	0.920	0.885	0.911	0.847	0.856
F1 score		0.835	0.829	0.904	0.906	0.928	0.919	0.893	0.908	0.859	0.855
Subset accuracy		0.688	0.739	0.834	0.841	0.847	0.854	0.841	0.847	0.726	0.783
Macro	Precision	0.921	0.744	0.940	0.941	0.962	0.945	0.967	0.903	0.927	0.806
	Recall	0.741	0.777	0.881	0.871	0.887	0.879	0.854	0.881	0.791	0.787
	F1	0.801	0.758	0.902	0.897	0.921	0.905	0.905	0.889	0.844	0.794
Micro	Precision	0.864	0.822	0.904	0.907	0.943	0.919	0.953	0.904	0.901	0.857
	Recall	0.829	0.832	0.910	0.910	0.925	0.922	0.885	0.913	0.850	0.857
	F1	0.846	0.827	0.907	0.908	0.934	0.921	0.918	0.909	0.875	0.857

Note:

The best classification performance (based on different criteria) is indicated in bold for each technique.