Abstract: Classification forecasting is an important work of data mining and machine learning, imbalanced data is a common problem in classification in most real domains.This paper proposes a new hybrid method for preprocessing imbalanced datasets (ImSMOTE-RSTR*) through the construction of new samples, using the improved Synthetic Minority Oversampling Technique together with the application of an editing technique based on the Rough Set Theory and the lower approximation of a subset.The proposed method has been validated by an experimental study showing good results as the learning algorithm.