  Machine learning models may involve decision boundaries that change over time
due to updates to rules and regulations, such as in loan approvals or claims
management. However, in such scenarios, it may take time for sufficient
training data to accumulate in order to retrain the model to reflect the new
decision boundaries. While work has been done to reinforce existing decision
boundaries, very little has been done to cover these scenarios where decision
boundaries of the ML models should change in order to reflect new rules. In
this paper, we focus on user-provided feedback rules as a way to expedite the
ML models update process, and we formally introduce the problem of
pre-processing training data to edit an ML model in response to feedback rules
such that once the model is retrained on the pre-processed data, its decision
boundaries align more closely with the rules. To solve this problem, we propose
a novel data augmentation method, the Feedback Rule-Based Oversampling
Technique. Extensive experiments using different ML models and real world
datasets demonstrate the effectiveness of the method, in particular the benefit
of augmentation and the ability to handle many feedback rules.
