Can I do bagging method as improvement technique to decision tree in research?
Let's clarify a few things first:

The bagging technique is an ensemble method which is not specific to decision trees, it can be applied to any classification method.
It's worth noting that there is another ensemble method specifically for decision trees, it's called Random Forest. While it's not the same method, it is known to generally improve performance compared to a regular Decision Tree algorithm like C4.5.

These techniques exist because they have been proved to improve performance in general, but whether they would improve performance on a specific problem (and by how much) has to be tested.
Also just to be clear: these techniques are already established, so using them wouldn't be an original research contribution in the field of Machine Learning. Their application to a new problem in a specific research domain might be a contribution to this domain, but that depends on the specific context.