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Overall, our performance evaluations metrics are typically much higher when evaluating clusters derived from real networks as compared with those derived from random networks, demonstrating the strength of our measures.
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The developed estimation models are evaluated using different evaluation metrics.
Another concern is the evaluation metrics selection when evaluating statistical models built on HTS data.
Algorithms can then be evaluated using classical extrinsic clustering evaluation metrics [52].
To evaluate the performance of our 4D FAPOA approach, we applied different standard evaluation metrics.
Results evaluated with respected to the datasets are assessed in terms of several evaluation metrics.
First, we present the used evaluation metrics.
Real data set description and evaluation metrics.
Table 2 gives the evaluation metrics.
The evaluation metrics include the following.
Five ensemble methods and various evaluation metrics are adopted.
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