Is it possible of an imbalanced dataset to benefit from the results of the same dataset after balancing? I am new to data science & machine learning
I am using Weka platform to work on a classification problem with an imbalanced dataset.
My question is: can I apply a feature selection method to a balanced copy of the dataset
then I use the resulted subset of features in the original dataset (imbalanced)?
I my question is not clear, I will explain it by the following detail steps:

*

*I made two copies of the dataset, the original imbalanced dataset and the balanced dataset.

*I applied a feature selection method to the balanced copy.
(at the end of this step, a subset of features is selected)

*In the imbalanced copy, I retained the selected features and removed the unselected ones.

For example:
Assume that you have an imbalanced dataset with 5 features: a, b, c, d, and e features.
You balanced the entire dataset. Then you applied a feature selection method. You got three selected features: a, b, and c. After that, you went back to the imbalanced dataset (the original one) and removed d and e features. Then you completed your procedures on the imbalanced dataset with a, b, and c features.
Is this procedure correct?

It is usually best to train a model on a sample that is close in distribution to the population you later want to apply the trained model to. Of course, some deviations between the distributions will always happen, simply by sampling variance. However, strong differences between the two datasets can lead to a model that is biased, i.e., systematically wrong. This can happen, for instance, if you purposely pick a training sample that is balanced (often called "oversampling the minority class" and similar). See Are unbalanced datasets problematic, and (how) does oversampling (purport to) help?
