How can I properly split imbalanced dataset to train and test set?
Start from 50/50 and go on changing the sets as 60/40, 70/30, 80/20, 90/10. declare all the results and come to some conclusion. In one of my work on Flight delays prediction project, I used 60/40 database and got 86.8 % accuracy using MLP NN.
I have a flight delay dataset and try to split the set to train and test set before sampling. On-time cases are about 80% of total data and delayed cases are about 20% of that. 
Normally in machine learning ratio of train and test set size is 8:2. But the data is too imbalanced. So considering extreme case, most of train data are on-time cases and most of test data are delayed cases and   accuracy will be poor. 
So my question is How can I properly split imbalanced dataset to train and test set??