Want to Add More Sample in Majority Class
Any oversampling technique should work just fine. 

For example, SMOTE is a technique for generating samples for a specific class. It works by selecting a class, finding the closest samples and generating one at a random position between them. The algorithm doesn't explicitly require those samples be from the minority class. Theoretically, you can apply this principle to the majority class as well. 

You can see more details about oversampling techniques (including SMOTE) from this jupyter notebook, which implements logic behind manually.