If you have an imbalanced dataset, the typical strategies to train a classifier are to oversample the minority class, or modify the loss function to penalize mis-classifications of the minority class more than mis-classifications of the majority class. If you think about it, these methods are mathematically equivalents?

Based on experiments that I ran with synthetic data, these methods are not very effective either. Yet they are widely used. Am I missing something?