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Table 2 The mean of the accuracy on data stream Hyperplane1

From: Comparative study between incremental and ensemble learning on data streams: Case study

  Data-chunk size 500 1000 2000
Classifier number Algorithm    
10 SEA 83.66±2.49 84.23±2.01 86.39±0.35
  AWE 92.88±0.11 93.34±0.05 93.61±0.10
20 SEA 85.82±3.14 87.06±0.53 87.49±0.79
  AWE 93.33±0.12 93.63±0.06 93.79±0.09
30 SEA 84.23±2.01 86.94±1.62 88.14±0.27
  AWE 93.49±0.10 93.70±0.06 93.85±0.08
  1. SEA get the optimal accuracy at a 2,000 data chunk size and 30 base classifiers, while AWE algorithm get optimal accuracy at a 2,000 data chunk size and 30 base classifiers.