BMC Bioinformatics

Table 3 Sensitivities and specificities of the swine flu classifier combinations

From: A Bayesian method for comparing and combining binary classifiers in the absence of a gold standard

		Sensitivity			Specificity
2^K-bit code	Combination	Mean	Median	SD	Mean	Median	SD
1	All results are negative	0.000	0.000	0.000	1.000	1.000	0.000
2	C₁∧C₂	0.626	0.633	0.162	0.991	0.994	0.010
3	¬C₁∧C₂	0.116	0.101	0.088	0.938	0.947	0.047
4	C ₂	0.742	0.760	0.152	0.928	0.939	0.054
5	C₁∧¬C₂	0.214	0.201	0.127	0.879	0.885	0.072
6^†	C ₁	0.840	0.859	0.119	0.870	0.875	0.078
7	(¬C₁∧C₂)∨(C₁∧¬C₂)	0.330	0.340	0.124	0.817	0.821	0.079
8^‡	C₁∨C₂	0.957	0.974	0.050	0.808	0.813	0.087
9	¬(C₁∨C₂)	0.043	0.026	0.050	0.192	0.187	0.087
10	(C₁∧C₂)∨¬(C₁∨C₂)	0.670	0.660	0.124	0.183	0.179	0.079
11	¬C₁	0.160	0.141	0.119	0.130	0.125	0.078
12	¬C₁∨C₂	0.786	0.799	0.127	0.121	0.115	0.072
13	¬C₂	0.258	0.240	0.152	0.072	0.061	0.054
14	C₁∨¬C₂	0.884	0.899	0.088	0.062	0.053	0.047
15	¬(C₁∧C₂)	0.374	0.367	0.162	0.009	0.006	0.010
16	All results are positive	1.000	1.000	0.000	0.000	0.000	0.000

SD: Standard Deviation.
^‡Optimal combination using ranking criteria 1, 2 and 3.
^†Optimal combination using ranking criterion 4.

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ISSN: 1471-2105

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