Can sampling like SMOTE/UP/DOWN applied on Validation set? I am trying to predict classification problem. For that I have used Ranger, Xgboost and naive bayes. My Response class is imbalance . 92:8 ratio. My positive Response is only 8% of whole data.

Because of class imbalance I am getting more FP / FN.

I tried different sampling on training set and predicted with original set gives me more FP. 

Can I do something like ?:
- split data into train and test
- apply sampling method individually on train and test
- and use sampled test to predict.

Thanks.
Oversampling on your test set will only artifically improve your performance. What you may want to do instead is changing your objective function to give more importance to you imbalanced class. There are already a lot of question about class imbalance on this website, such as : Classification problem: custom minimization measure