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A global hypothesis test has been studied in order to simultaneously compare the predictive values of two or more binary diagnostic tests when the binary tests and the gold standard are applied to all of the individuals in a random sample.
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In order to estimate the performance of the binary tests, we used the modeled latent M. bovis infection status of each animal to calculate sensitivities and specificities of the respective tests.
Each patch is guided by the binary tests stored at the nodes.
The results of the experiments with the dual-target activity profiles are depicted in Figure 14 for the binary test sets and in Figure 15 for the elaborated labeling described in Table 2. Once again, the results are in line with the aforementioned findings.
For efficiency reason, the number of binary tests is determined depend on the depth of the tree.
We used paired t-tests and binary tests of proportions to compare each prenatal urinary metabolite measurement with the postpartum measurement.
The resulting optimum binary test improves the discriminative power of individual trees in the forest.
That is, the number of the binary test increases with increasing the depth of the tree.
Considering the pattern of preference recorded in binary tests, we analyzed our data splitting the females into two sub-groups according to the preference expressed in these tests.
Verification bias has been studied in binary tests [ 13, 14], and in ordinal tests [ 15, 16].
By instrumenting the binary under test and applying the quality evaluation of test cases introduced in Section 4, we are able to feedback coverage information of currently explored input regions to the test case generation algorithm.
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