Learning in non-stationary environments with class imbalance
Learning in non-stationary environments is an increasingly important problem in a wide variety of real-world applications. In non-stationary environments data arrives incrementally, however the underlying generating function may change over time. In addition to the environments being non-stationary, they also often exhibit class imbalance. That is one class (the majority class) vastly outnumbers the other class (the minority class). This combination of class imbalance with non-stationary environments poses significant and interesting practical problems for classification. To overcome these issues, we introduce a novel instance selection mechanism, as well as provide a modification to the Heuristic Updatable Weighted Random Subspaces (HUWRS) method for the class imbalance problem. We then compare our modifications of HUWRS (called HUWRS.IP) to other state of the art algorithms, concluding that HUWRS. IP often achieves vastly superior performance.