Accuracy for stream-learn data stream (1).

Accuracy for stream-learn data stream (1).

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Modern analytical systems must be ready to process streaming data and correctly respond to data distribution changes. The phenomenon of changes in data distributions is called concept drift, and it may harm the quality of the used models. Additionally, the possibility of concept drift appearance causes that the used algorithms must be ready for the...

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... of the experiments, we compare the performance of the proposed method to baseline. Results were collected following the experimental protocol described in the previous sections. To save space, we do not provide results for all models and streams. Instead, we plot accuracy achieved by models on selected data streams. These results are presented in Fig. 4, 5, 6, and 7. All learning curves were smoothed using a 1D Gaussian filter with σ = ...
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... also observe larger gains from applying CAR on streams with bigger chunk size. To illustrate please compare results from Fig. 4 to Fig. 5. One possible explanation behind this trend is that gains obtained from employing CAR are proportional to the difference in size between the base and drift chunk size. In our experiments, drift chunk size was equal to 30 for all streams and models. This explanation is also in line with the results of hyperparameter ...

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