For detection problems evaluated using F-measure, our framework proves that the utility of correctly predicting one more positive instance (marginal positive utility) and that of correctly predicting one more negative instance (marginal negative utility) are different and dynamically changed during model training.
the importance of instances of each class is not only determined by their data distribution, but also affected by how well the current model can converge on different classes.