Learning in non-stationary environments with class imbalance
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
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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. While there is a variety of research into such environments, the research mainly consists of detecting concept drift (and then relearning the model), or developing classiï卢聛ers which adapt to drift incrementally. We introduce Heuristic Up datable Weighted Random Subspaces (HUWRS), a new technique based on the Random Subspace Method that detects drift in individual features via the use of He llinger distance, a distributional divergence metric. Through the use of subspaces, HUWRS allows for a more ï卢聛ne-grained approach to dealing with concept drift which is robust to feature drift even without class labels. We then compare our approach to two state of the art algorithms, concluding that for a wide range of datasets and window sizes HUWRS outperforms the other methods.