An ensemble based incremental learning framework for concept drift and class imbalance

We have recently introduced an incremental learning algorithm, Learn
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.NSE, designed to learn in nonstationary environments, and has been shown to provide an attractive solution to a number of concept drift problems under different drift scenarios. However, Learn
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.NSE relies on error to weigh the classifiers in the ensemble on the most recent data. For balanced class distributions, this approach works very well, but when faced with imbalanced data, error is no longer an acceptable measure of performance. On the other hand, the well-established SMOTE algorithm can address the class imbalance issue, however, it cannot learn in nonstationary environments. While there is some literature available for learning in nonstationary environments and imbalanced data separately, the combined problem of learning from imbalanced data coming from nonstationary environments is underexplored. Therefore, in this work we propose two modified frameworks for an algorithm that can be used to incrementally learn from imbalanced data coming from a nonstationary environment.