What is the correct way to compute Mean F1 score? I have a set of 10 experiments that compute precision, recall and f1-score for each experiment.
Now, average precision &amp; average recall is easy to compute. I have some confusion regarding average f1-score.

There are 2 ways on how i can compute mean f1-score:  


Take f1 scores for each of the 10 experiments and compute their average. 
Take average precision &amp; average recall and then compute f1-score using the formula f1 = 2*p*r/(p+r)  


I could not find any strong reference to support any of the arguments. The closest document i could find is this: https://www.kaggle.com/wiki/MeanFScore 

Can anyone explain with some reference (if possible) which of the methods is correct and why? 

EDIT: One of the members suggested this source. Though, i still suspect the reliability of the source. I have seen people not using the method explained above in their research publications. (Even i would be using it in one of my publications) I would expect some more opinions from the community to verify this idea.
This paper Apples-to-apples in cross-validation studies: pitfalls in classifier performance measurement by Forman and Scholz discuss the different methods for computing the average F-score in cross validation. The paper shows that under very high class imbalance some of the computation methods (average of individual folds F-scores or F-score based on average of individual folds precision and recall) can lead to biased results. The paper recommends computing the F-score by adding the TP, FP, FN from each fold, computing the precision and recall and finally the F-score.