Learning in a non-stationary environment and in the presence of class imbalance has been receiving more recognition from the computational intelligence community, but little work has been done to create an algorithm or a framework that can handle both issues simultaneously. We have recently introduced a new member to the Learn++ family of algorithms, Learn++.NSE, which is designed to track non-stationary environments. However, this algorithm does not work well when there is class imbalance as it has not been designed to handle this problem. On the other hand, SMOTE - a popular algorithm that can handle class imbalance - is not designed to learn in nonstationary environments because it is a method of oversampling the data. In this work we describe and present preliminary results for integrating SMOTE and Learn++.NSE to create an algorithm that is robust to learning in a nonstationary environment and under class imbalance.