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Modern analytical systems must be ready to process streaming data andcorrectly respond to data distribution changes. The phenomenon of changes indata distributions is called concept drift, and it may harm the quality of theused models. Additionally, the possibility of concept drift appearance causesthat the used algorithms must be ready for the continuous adaptation of themodel to the changing data distributions. This work focuses on non-stationarydata stream classification, where a classifier ensemble is used. To keep theensemble model up to date, the new base classifiers are trained on the incomingdata blocks and added to the ensemble while, at the same time, outdated modelsare removed from the ensemble. One of the problems with this type of model isthe fast reaction to changes in data distributions. We propose a new ChunkAdaptive Restoration framework that can be adapted to any block-based datastream classification algorithm. The proposed algorithm adjusts the data chunksize in the case of concept drift detection to minimize the impact of thechange on the predictive performance of the used model. The conductedexperimental research, backed up with the statistical tests, has proven thatChunk Adaptive Restoration significantly reduces the model's restoration time.

Employing chunk size adaptation to overcome concept drift