Online Ensemble Using Adaptive Windowing for Data Streams with Concept Drift

Abstract

Data streams, which can be considered as one of the primary sources of what is called big data, arrive continuously with high speed. The biggest challenge in data streams mining is to deal with concept drifts, during which ensemble methods are widely employed. The ensembles for handling concept drift can be categorized into two different approaches: online and block-based approaches. The primary disadvantage of the block-based ensembles lies in the difficulty of tuning the block size to provide a tradeoff between fast reactions to drifts. Motivated by this challenge, we put forward an online ensemble paradigm, which aims to combine the best elements of block-based weighting and online processing. The algorithm uses the adaptive windowing as a change detector. Once a change is detected, a new classifier is built replacing the worst one in the ensemble. By experimental evaluations on both synthetic and real-world datasets, our method performs significantly better than other ensemble approaches.