An Incremental Learning Algorithm for Non-stationary Environments and Class Imbalance

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
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family of algorithms, Learn
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.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 over sampling the data. In this work we describe and present preliminary results for integrating SMOTE and Learn
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.NSE to create an algorithm that is robust to learning in a non-stationary environment and under class imbalance.