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

Journal of Big Data

Table 2 The mean of the accuracy on data stream Hyperplane1

	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

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.