I would like to know how to interpret a difference of f-measure values. I know that f-measure is a balanced mean between precision and recall, but I am asking about the practical meaning of a difference in F-measures.

For example, if a classifier C1 has an accuracy of 0.4 and another classifier C2 an accuracy of 0.8, then we can say that C2 has correctly classified the double of test examples compared to C1. However, if a classifier C1 has an F-measure of 0.4 for a certain class and another classifier C2 an F-measure of 0.8, what can we state about the difference in performance of the 2 classifiers ? Can we say that C2 has classified X more instances correctly that C1 ?